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Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: A review
Journal Pre-proofs Review Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: a review Runmei Ma, Jie Ban, Qing Wang, Tiantian Li PII: DOI: Reference:
S0048-9697(19)34454-7 https://doi.org/10.1016/j.scitotenv.2019.134463 STOTEN 134463
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Science of the Total Environment
Received Date: Revised Date: Accepted Date:
29 June 2019 28 August 2019 13 September 2019
Please cite this article as: R. Ma, J. Ban, Q. Wang, T. Li, Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: a review, Science of the Total Environment (2019), doi: https:// doi.org/10.1016/j.scitotenv.2019.134463
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Statistical
spatial-temporal
modeling
of
ambient
ozone
exposure
for
environmental epidemiology studies: a review Authors: Runmei Ma1, Jie Ban1, Qing Wang1, Tiantian Li1*
1
National Institute of Environmental Health, Chinese Center for Disease Control and
Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
Correspondence to: Tiantian Li, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. Email: [email protected], Telephone: 8610-50930211
1
Abstract Background Studies have discovered the adverse health impacts of ambient ozone. Most epidemiological studies explore the relationship between ambient ozone and health effects based on fixed site monitoring data. Fine modeling of ground-level ozone exposure conducted by statistical models has great advantages for improving exposure accuracy and reducing exposure bias. However, there is no review summarizing such studies.
Objectives A review is presented to summarize the basic process of model development and to provide some suggestions for researchers.
Methods A search of PubMed, Web of Science and the Wanfang Database was performed for dates through July 1, 2019 to obtain relevant studies worldwide. We also examined the references of the articles of interest to ensure that as many articles as possible were included.
2
Results The land use regression model (LUR model), random forest model and artificial neural network model have been used in this field. We summarized these studies in terms of model selection, data preparation, simulation scale selection, and model establishment and validation. Multiparameters are a major feature of models. Parameters that influence the formation of ground-level ozone concentrations and parameters that have been extremely important in previous articles should be considered first. The process of model establishment and validation is essentially a process of continuously optimizing the model performance, but there are certain differences in the specific models.
Conclusion This review summarized the basic process of the statistical model for ambient ozone exposure. We gave the applicable conditions and application scope of different models and summarized the advantages and disadvantages of various models in ozone modeling research. In the future, research is still needed to explore this area based on its own research purposes and capabilities.
Key words: ambient ozone; statistical model; exposure assessment; environmental epidemiology 3
1 Introduction As a secondary air pollutant, ambient ozone is a concern due to its widespread pollution (Anenberg et al. 2010). The adverse health effects of ground-level ozone have been proven by many international studies, and the outcomes have showed varied effects, such as the mortality of all non-accidental causes, cardiovascular, respiratory and coronary diseases, and hypertension (Yin et al. 2017; Bell et al. 2014; Wong et al. 2008; Peng et al. 2013; Di et al. 2017b; Yang et al. 2017). The harmful effects of ozone have also been tested at more sophisticated levels by using, for example, glucose (Yang et al. 2018), blood pressure (Hunter et al. 2018) and inflammatory markers (Lee et al. 2018). Therefore, ambient ozone causes serious public health problems. Exposure assessments of ozone are key to exploring the relationship between ambient ozone and health.
For environmental epidemiological studies that focus on the health effects caused by ambient ozone, there is a great demand for the accurate assessment of ambient ozone exposure. For long-term exposure studies, high-resolution exposure data for historical periods were sparse, while for short-term exposure studies, many regions lack continuous daily ozone data from monitoring sites. Monitoring data have been commonly used in previous studies, but they are insufficient at the spatial and temporal scales and thus result in incomplete exposure assessment. Even when the monitoring data are complete, using the data to represent personal exposure results in 4
errors caused by the low precision of instruments or the gap between averaged individual exposure and the true level of air pollutants. Some studies further improved exposure accuracy by matching monitoring data based on home address, but Berkson error still exists due to differences between personal exposure level and averaged individual exposure measurements. To solve the above problems, some studies have explored the use of models to estimate ground-level ozone exposure at high spatial and temporal resolutions to try to avoid exposure assessment errors as much as possible. In recent years, focusing on the adverse health impacts of ozone and the accumulation of various data (satellite data, chemical transport modeling data, etc.), studies have used multiparameter statistical models to simulate ground-level ozone exposure, and the results have been applied to epidemiological studies; however, a review of these studies has not been conducted.
The objectives of this review are to discuss recent ground-level ozone statistical models for environmental epidemiology studies and to provide suggestions for future studies. We will provide a brief introduction of the models mostly used in this field. In this review, four factors will be considered: model selection, data preparation, simulation scale determination, and model development and validation. Applications and limitations will also be discussed.
2 Methods 5
To include a wide range of relevant studies, we searched important databases in English and Chinese, including PubMed, Web of Science and the Wanfang Database. The search was performed for dates through July 1, 2019 to obtain relative studies worldwide. The search words included were “ozone”, “O3”, “estimate”, “predict”, “forecast”, “spatiotemporal”, “spatial”, and “temporal”, which were searched in both English and Chinese. We also examined the references of the articles of interest to ensure that as many articles as possible were included.
The exclusion criteria for the review are as follows: 1) studies that do not mainly rely on mathematical statistics theories, such as chemical transport models (CTMs) and air quality models; 2) unspecified descriptions of the parameters and conducting process and unclear results; and 3) literature with repeated reports (Figure 1).
3 Results Based on a preliminary analysis of the literature, we found three types of models that have been widely used: the land use regression model (LUR model), the random forest model and the artificial neural network model. Detailed information of the studies included in the review is shown in Table 1.
3.1 Land use regression model 6
The LUR model is a multivariate regression model established by the least squares method to simulate the spatial distribution of the air pollutant concentrations in the study area. The model integrates land use data and traffic and population density data around the monitoring site into the geographic information system (GIS) to analyze the relationship between these parameters and the spatial distribution of pollutant concentrations to estimate the concentration at any location that does not have monitoring data (Wu et al. 2016). The variables are generated by setting an increasing buffer distance and selected according to the backward algorithm and the forward algorithm. Based on pollutant concentrations and geographic variables, a multivariate regression model was established by the least squares method.
LUR models were introduced in the air pollution modeling field in 1997 (Briggs et al. 1997), and they are normally used in regression mapping for ambient particulate matter, NOx or VOCs (Hoek et al. 2008). To the best of our knowledge, the earliest studies using LUR models to simulate ambient ozone exposure were conducted in 2009 (Beelen et al 2009). For the spatial scale, in some ways, LUR models mainly focus on a certain city or region, such as Quebec (Adam-Poupart et al. 2014) in Canada or metropolitan areas in the United States (Wang et al. 2015). However, some studies have modeled ground-level ozone exposure at a broader scale, such as Beelen et al. (2009), which was conducted in Europe. For the temporal scale, studies that have focused on long-term exposure are generally based on the annual mean value 7
(Beelen et al. 2009; Kerckhoffs et al. 2015; Wolf et al. 2017; De et al. 2018; Huang et al. 2017), and studies that have focused on short-term exposure have scattered hourly values (Son et al. 2018), 8-h averages (Adam-Poupart et al. 2014), daily means (Malmqvist et al. 2014) and two-week concentrations (Wang et al. 2015, 2016). Most LUR models applied in large metropolitan areas with high spatial resolutions (Hoke et al., 2008), such as the studies of the metropolitan areas of Mexico City, with a resolution of 30 m×30 m (Son et al. 2018), or Wang et al.(2015)’s research, which was based on six metropolitan areas in America and used a resolution of 50 m×50 m. According to the parameters used in various studies, commonly used parameters include land use term, traffic density, population density, emissions inventory and meteorological parameters. Most studies evaluated the performance of the model through the correlation coefficient (R2) or root mean square error (RMSE). The performances were generally good, with R2 values up to 0.92 (Augsburg, Germany, 1 km×1 km, hourly) (Wolf et al. 2017). The LUR model has been widely used in exposure assessments in Europe and North America, and the simulation results have been used in epidemiological studies (Holle et al. 2005).
3.2 Random forest model The random forest model is an algorithm based on a classification tree composed of a tree classifier {h (x, Θ k), k = 1,..}, where {Θ k} is an independent identical distribution random vector and each tree votes on x. The final results are obtained by 8
averaging the prediction results of the regression tree for multiple subsample sets through several rounds of training (Yang et al. 2014).
Random forest models have been widely used to simulate the spatial and temporal distributions of air pollutants (Hu et al. 2017; Zhan et al. 2017), but few studies have used random forest models in ambient ozone exposure simulations. Zhan et al. (2018) assessed the ambient ozone exposure intensity and duration in China in 2015. Meteorological data, elevation data, emissions inventories, land use data, vegetation indexes, road density data and population density data were prepared based on 0.1° × 0.1° resolution to model the daily maximum 8-h mean ozone concentration. The contribution of meteorological parameters was found to be 65%, and the contribution of evaporation, which is a comprehensive index of temperature and humidity and has an important influence on the formation and stability of ozone, was especially notable. The authors also compared results with the spatiotemporal kriging interpolation method, showing that the performance of the random forest model was slightly higher (the R2 of the random forest model was 0.69, while the R2 of the interpolation method was 0.68).
3.3 Artificial neural network model The artificial neural network is a nonlinear complex network system composed of a 9
large number of simple neurons. In the learning process of the neural network, the connection and topology of each neuron are constantly changing under the external stimulus until the network output gradually approaches the expected output. The model can be divided into a feedforward neural network and a feedback neural network. With continuous research, deep learning has become a new development in neural network research. Convolutional neural networks, as a type of deep feedforward neural network model, reduce the complexity of network models, have the ability to perform hierarchical learning and can accurately extract features (Chang, 2013; Lei, 2018).
Similar to the random forest model, the artificial neural network has many advantages, but few studies implementing this method are currently used for ground-level ozone simulation. Di et al. (2017a) established a convolutional neural network model that considered neighboring information through the introduction of the convolutional layer and used remote sensing ozone products, nitrogen oxide and sulfur dioxide data from both monitoring sites and satellite-based products, volatile organic compound (VOC) data from monitoring sites, chemical transport model outputs, meteorological data and land use variables to simulate near-surface ozone exposure based on a 1 km×1 km grid in the United States from 2000 to 2012. R2 reached 0.76, which indicates good performance. These predictive results were used in epidemiological studies that assessed the relationship between ozone and mortality in the U.S. from 10
2000 to 2012 (Di et al. 2017b).
4 Discussion We have reviewed the multiparameter statistical models used to simulate high-resolution ground-level ozone exposure. This review is of great significance for the development of future epidemiological studies and facilitates the use of accurate exposure assessment methods for subsequent studies. Furthermore, this review can provide a basic understanding from some perspectives for the introduction of policies and management measures to prevent and control the adverse effects of ambient ozone on human health. We will introduce the basic process of developing research that simulates ground-level ozone exposure using statistical models and compare the differences between studies to provide suggestions for future studies.
4.1 Model selection The most suitable model should be chosen after considering the study purpose, study area, data availability, and computing resources.
Currently, the LUR model is more widely used than the random forest and artificial neural network models, and the reasons are discussed as follows. First, the models
11
have undergone long-term development, and all aspects of the technology are mature, with fewer types of parameters, simpler operations and higher spatial resolutions. Furthermore, this model uses practical experience by setting parameter weights to establish models, which can avoid unreasonable results (Shi and Wang, 2016). However, it also has disadvantages, such as unstable modeling methods and poor spatial and temporal migration (Hoek et al. 2008). Different urban structures may result in different model performances for the same model. We found that the LUR model was more likely to be used in regions instead of whole countries because it has difficulty forming a prediction for long-term time series due to the search for high resolution. The LUR model has difficulty capturing small spatial changes in concentrations when the parameters of the study area are not very variable. When the change is too large, the modeled concentration tends to be higher than the true exposure, and the correlation is poor (Briggs et al. 2000).
The random forest and neural network models are black-box models that explore the relationship between ozone exposure and different variables and realize the rapid calculation of large-scale pollution simulations. The random forest model improves performance without drastically increasing computational complexity. In addition, it can obtain the order of parameter contributions, has high insensitivity to multivariate collinearity, and has high stability with missing and unbalanced data; thus, it can explain the influences of thousands of variables and does not need to be normalized 12
(Li, 2013). Furthermore, random forest models have certain advantages that can help to avoid overfitting: a model with more trees obtains a limited generalization error (Breiman et al. 2001; Shu X. 2013). However, it is not easy to explain the results during the process because of random forest models are black-box models. The artificial neural network has excellent performance and wide adaptability; however, it converges slowly and easily overfits high-dimensional features, and a large number of calculations results are required by the research team, which burdens the computing platform. The random forest model and artificial neural network are more advantageous than the regression model when handling multiparameter data, and they have prediction advantages for long-term time series and large study sites.
After obtaining a good understanding of the basic research situation, including the study purpose and the advantages and disadvantages of different models, researchers can choose the appropriate model according to their own needs. On this basis, parameters can be collected and prepared.
4.2 Data preparation 4.2.1 Monitoring data The research included in this review shows a very large difference in the area of the study region, from national scale to local scale, which means that the number of 13
monitoring sites varies widely. Instead of routine monitoring networks, some investigators set passive samplers on purpose or select stations for special design (Wolf et al. 2017; Malmqvist et al. 2014; Wang et al. 2015, 2016; Kerckhoffs et al. 2015). Purpose-designed monitoring sites normally consider population exposure. The researchers divide the sites into categories, such as regional background, urban background, traffic sites and industrial sites, or simply set monitoring sites at the location of the subjects. Wang et al. (2015) compared the difference between fixed sites and purpose-designed samplers: the R2 value for fixed locations ranged from 0.65 to 0.88, while for the predictions at home sites, the R2 value was between 0.60 and 0.91. Using purpose-designed monitoring or multiple sources of monitoring data can improve the ability to capture spatiotemporal variations in the model on a small scale, but it cannot adapt to the requirements for conducting large-scale studies.
In addition to differences in the number and type of monitoring network, the length and continuity in the time series of the monitoring data are also different. The metric used in the model should consider future applications. For health effects caused by long-term exposure, complete time series are more important than high temporal resolutions for the modeled results. For example, Wang et al. (2015) simulated two-week, ground-level ozone exposure from 1999–2013 in America. Due to the sample period, the weekly time frame for the monitoring data limited the prediction ability for 8-h maximum values that are commonly used, but investigators believed 14
that the two-week time scale can reduce the impact of meteorological factors and time autocorrelation. For studies focusing on acute health effects, high-temporal-resolution metrics are needed. LUR models often fail to form long-term time series in pursuit of a high spatial resolution, which is one of the issues that researchers should consider when choosing models. For studies that include long time scales and high resolutions, the machine-learning method is better.
4.2.2
Parameter selection and preparation
Multiparameters are a major feature used to simulate ambient ozone concentrations based on statistical models. They can extend the variable category and adjust the variable form. The explanatory ability of the model can be improved, and the influences of specific variables can be highlighted (Wu et al. 2016). The multiparameter trend for ambient ozone simulation is definite. Thus, parameters can be considered in several ways.
First, the mechanism of ground-level ozone formation should be considered. Ground-level ozone is a typical secondary atmospheric pollutant and is formed by a series of photochemical reactions of nitrogen oxides (NOx) and VOCs under ultraviolet light at a suitable temperature. A variety of typical factors directly affect the formation of ground-level ozone, including the concentration of precursor 15
materials, meteorological factors, and traffic-related factors affecting propagation. Socioeconomic, land use, vegetation coverage and other factors also have a certain impact on the formation and diffusion of the ground-level ozone concentration (Chen et al. 2017). More specifically, the parameters can include 1) meteorological parameters such as wind speed, wind direction, solar radiation, temperature, rainfall, and relative humidity, 2) geographic variables such as land use data, green space data, traffic data, residential and population density data, regional indicators, elevation, and distance from the sea, 3) emissions inventory parameters such as NO, NO2, organic carbon, NOx, VOCs, SO2 and particulate matter, and 4) monitoring site parameters such as NOx, sulfur oxides, VOCs and other pollutants at the ground monitoring site. Data products reflecting the formation, distribution and simulation of ground-level ozone concentrations, such as satellite data products provided by open platforms, such as the National Aeronautics and Space Administration (NASA) and CTM products from previous studies, were also used in models to improve performance.
Furthermore, after considering factors that influence the formation of ground-level ozone, new researchers can be inspired by previous research on the contribution of parameters. The contributions of variables vary by study. Zhan et al. (2018) found that meteorological variables have the highest relative importance, accounting for 65%, with the highest contributions being attributed to evaporation, temperature and humidity. Son et al. (2018) found that detailed emission patterns from local pollution 16
sources, coupled with wind field data (wind speed, wind direction and boundary layer height), are needed to improve current LUR models. Altitude (Beelen et al. 2009; Wang et al. 2016), road density, residential land (Beelen et al. 2009), green space and primary emission feature (traffic, population and impervious surfaces) (Wang et al. 2016) data are also contributors. Furthermore, Wang et al. (2016) and De et al. (2018) found that the use of CTMs results in models with improved performance. Some variable contributions were opposite in different studies. For example, Wang et al. (2015) found that smaller-scale GIS covariates, such as road network and population density, cannot represent the characteristics of ambient ozone well, which may lead to poor performance of the ozone simulation model. More kinds of parameters can be involved in the model, and the appropriate parameters can be selected during the model development process.
After parameter selection, the process of preparing key parameters varied in previous articles. The specific metrics of some variables were different based on data availability and the aims of the investigators. Taking traffic data as an example, a multicenter study conducted in Europe found that there was no high-resolution European database for roads, and traffic flow data were not consistently available in these countries; thus, road length was the only traffic variable in the model (Beelen et al. 2009). Some studies calculated conclusive variables to represent local traffic conditions, like traffic density. However, the definition of traffic density is not the 17
same in each study. Most LUR studies considered traffic density to be a long-term variable (Hoek et al. 2008). Adam-Poupart et al. (2014) defined traffic density as kilometers of road within a circular area with a 1-km radius, while Son et al. (2018) manually coded traffic density from the Google traffic website, and Kerckhoff et al. (2015) used traffic counts multiplied by 48 to obtain traffic intensities during daytime hours and then by 1.29 to calculate traffic intensities per 24-hour period. Some studies used multiple variables instead of conclusive variables, including road length, distance to nearby major roads and, within buffers, lengths of roads and truck routes and counts of intersections, to determine the specific relationship between traffic and ambient ozone (Wang et al. 2015). The quality of the raw traffic data was variable. Wang et al. (2015) used monitoring data from air quality system (AQS) monitors, which were purposefully placed away from roads, and studies using these data may have underestimated the effects of traffic indicators. Researcher considerations also impact data preparation. For example, Wolf et al. (2017) divided land use data into residential, industrial, built-up, urban green, forest, seminatural and water body areas, while Malmqvist et al. (2014) defined land use data as high-density residential, low-density residential, industrial, port, urban green, seminatural and forested areas. Four types of land use data were defined by Huang et al. (2017), including residential land, agricultural land, green spaces and water bodies.
The selection process mainly depends on the formation mechanism of ambient ozone 18
and previous studies or experience. Parameters commonly used in the model mainly include 1) meteorological variables such as temperature, humidity, evaporation, solar radiation, wind speed, boundary layer height, and precipitation, 2) precursor concentration, 3) geographical variables such as land use term, vegetation cover, and elevation, 4) emission inventory, 5) socioeconomic factors such as transportation, residential and population density, and 6) simulation products such as chemical transport model output (Table 1). Researchers can change the definitions of specific parameters based on their aims to obtain an optimal combination.
4.3 Simulation scale determination Due to the characteristics of data availability and study purposes, the time-scale simulation can be divided into continuous simulations and simulations for specific periods. Furthermore, the spatial and temporal resolutions can be divided into high and low resolutions. The high-spatiotemporal-resolution results can provide fine-concentration population exposure, which can improve accuracy and reduce conflicting factors, especially for studies focusing on acute health effects. However, this also increases the difficulty of data acquisition and processing and presents higher requirements for the hardware and software facilities of the research team. The low-spatiotemporal-resolution results can save time and manpower and obtain results more quickly. For long-term exposure studies that have low requirements for time-resolution results, researchers can choose metrics at the annual or monthly level 19
to improve effectiveness.
Based on study purpose and need, the model type can be chosen. For finer simulations, especially on a large study scale with complex trends, machine-learning methods performance better than the LUR model. Although studies that focused on ambient ozone exposure were rare, applications for other air pollutants proved superior in some contexts. Using PM2.5 as an example, Hu et al. (2017) used a random forest model in the United States in 2001 and obtained a simulation result of R2=0.8 based on a resolution of 1 km×1 km; Zhan et al. (2017) used a geographically weighted gradient lifting tree in China in 2014, and the result was obtained based on a spatial resolution of 0.5°×0.5° (R2=0.76). Di et al. (2016) used a convolutional neural network from 2000 to 2012 at a 1 km×1 km resolution and obtained a total R2 of 0.84; Li et al. (2017) used a neural network in China in 2015, with an R2 up to 0.88 based on a resolution of 0.1°×0.1°.
4.4 Model development and validation The process of model development varies based on model type. The land use model builds a linear regression model using a series of related variables to simulate ozone exposure. The random forest model can be regarded as a regression model when simulating ozone concentration by building relationships between independent and 20
dependent variables. The neural network freely learns any function form from the training data to simulate the ozone concentration. Di et al. (2017a) considered neighboring information during the modeling process through the introduction of a convolutional layer.
The processes of developing models based on regression algorithms and machine-learning algorithms are similar: model parameters and features are constantly adjusted to achieve optimal performance. Regression algorithms and machine-learning algorithms have similarities and differences in terms of model establishment and verification.
However, the development processes of the three models could be different. For the LUR model, parameter selection, which is mainly conducted through a supervised stepwise procedure, is important. Basically, a regression model is used for all potential predictors and the one with the highest explained variance is chosen. Then, researchers add further predictors step by step if the increase in adjusted R2 is >1%. Some researchers use a priori effect direction for each variable and require that the effect direction of the variable included must be the same as the a priori direction and that the effect direction of variables already in the model does not change. According to these researchers, not constraining a priori directions leads to models that make unrealistic predictions. A constraint was needed because there was considerable 21
collinearity between predictor variables (Beelen et al. 2009). Others had the opposite view: ozone, as a secondary pollutant, was involved in many reactions, and the effect directions were unclear (Malmqvist et al. 2014). Other researchers (Son et al. 2018) used the least absolute shrinkage and selection operator (LASSO) method, which aims to improve the prediction accuracy and interpretability of the model (Tibshirani, 1996) and the performance of the traditional LUR method. Developing a multivariate logistic regression model requires knowing the optimal number of prognostic factors to include. The LASSO method has a smaller mean square error (MSE) than conventional methods and handles the multicollinearity problem. Some researchers defined their own methods; for example, Wang et al (2015) developed a hierarchical spatiotemporal model to accommodate unique features and selected the best model based on the cross-validated R2 value. Furthermore, restrictions were proposed to remove variables in some situations. These restrictions aimed to improve the stability of the models (Wolf et al. 2017; Wang et al. 2015). For example, Wolf et al. set the following conditions: at least five sites must exhibit different values and the minimum or maximum values must lie within threefold of the 10th to 90th percentile range below or above the 10th and 90th percentiles. In this way, the selection of specific predictors that included mainly zeros or extreme outliers was prevented.
For the machine-learning method, like the integrated learning algorithm, parameter selection is mainly achieved by performing subsequent operations based on model 22
importance ordering of all parameters. The important parameters were number of variables included in every tree and the number of trees. Meanwhile, the deep learning algorithm has the ability to automatically learn features. The hidden layer number, hidden node number and active function should be set. These model parameters should be adjusted during the model development process depending on model performance, mainly reflecting R2, R, MSE, and RMSE, which are calculated during the validation process.
The main validation method is the cross-validation (CV) method, which can be divided into two types in these articles. The first type is leave-out-one cross validation (Wolf et al. 2017), which is the most common CV method (Geisser, 1974). In this field, the basic idea is that a monitoring station is excluded from the total number of sampling sites (K) to generate training datasets with K-1 sites, resulting in fitted models that simulate pollutant concentrations at the excluded sites (test set). This method is commonly used for the LUR model and has been criticized for overestimating predictive ability (Wang et al. 2013). The other method is V-fold CV (Son et al. 2018), where V=10, which means that the monitoring data are divided into 10 random splits, 9 for training the model and 1 for testing the model performance, until the best model performance is achieved, is the most common (Wang et al. 2015). Some researchers set the cut-off values at 75% and 25% (Beelen et al. 2009) or 80% and 20% (De et al. 2018). 23
In addition to model performance, the set of model parameters is also limited by the computing resources; for example, a higher tree number and number of steps for iteration would lead to better model performance in some contexts but also to a longer calculation time. The research team can set the parameters according to research purpose and ability and finally establish the optimal model at the current level.
4.5 Application of the simulation results A few studies modeled ground-level ozone exposure in Europe, North America and China, and some results have been used in epidemiological studies (Holle et al. 2005; Yang et al. 2018). The high resolution of modeled ambient ozone exposure data provided a chance, in some contexts, to effectively explore the relationship between ozone and human health.
Some studies achieved high model performance, but the robustness of the studies, i.e., what might happen when the model is extrapolated to other regions, should be considered. Some studies have found factors that influence the robustness of the models. The degree of urbanization and the distribution of the monitoring sites vary by region, so care should be taken when the data are extrapolated to other regions. For example, Wang et al. (2015) noted that models are limited when applied to areas 24
where monitoring networks were sparse; Adam-Poupart et al. (2014) found that estimates exhibit higher discrepancies from data values that are mostly from regions characterized by data scarcity. Kerckhoffs et al. (2015) found that, by excluding some observations, which increased the urbanization of sites, the robustness of the model remained good. Furthermore, Beelen et al. (2009) found that extrapolating far beyond the range of measured concentrations would lead to errors, with a possible solution being truncating the predicted concentrations to the highest measured concentration. To explore the relationship between ambient ozone and health more precisely, more high-resolution exposure assessments are needed, and care should be taken when assessment results are extrapolated from other places.
4.6 Prospects and limitations With the development of epidemiological studies, further studies are needed that focus on simulating ozone with high spatial and temporal resolutions. Complete time series and large coverage areas also provide convenience for the exposure assessments of populations on a fine scale. On one hand, the daily modeling data complemented the time series, especially provided historical exposure data; on the other hand, the high-spatial-resolution modeling results provide exposure data for areas without monitoring sites. Exposure data at high spatial and temporal resolutions improves exposure accuracy and reduces possible exposure assessment errors for both acute and chronic environmental epidemiological studies. The modeling results are 25
derived from statistical models based on a variety of parameters affecting ozone formation and elimination. The errors caused by relying solely on ozone monitoring instruments are reduced, as well as the errors due to the limited number and placement of monitoring stations. Furthermore, the modeling results with high spatial and temporal resolutions can better represent the spatiotemporal variability in the ambient ozone distribution and reduce the exposure error. Compared with developed countries, developing countries with shorter monitoring histories and rare distributions of monitoring air pollution stations have greater demand for these studies. Furthermore, considering the variance caused by the form of varied variables and the difficulties in comparing results, standard databases that are capable of multiple parameter requirements and shared platforms for researchers are inevitable.
Limitations still exist in this field. First, current studies that have focused on fine ambient ozone exposure modeling are limited and need to be continuously developed. Most studies have focused on low-pollution areas in developed countries, while few researchers have paid attention to highly polluted areas in developing countries, where monitoring stations are sparsely distributed and the monitoring history is not long. The demand for the fine-resolution assessment from epidemiological studies has not been completely met. Second, the validation of the modeling results remains a limitation. Availability and effectiveness should be tested not only by the model performance but also by the results of epidemiological studies that use modeling 26
results as exposure data. Few studies have evaluated error type and the amount of measurement error by comparing difference between true exposure and simulated exposure using different exposure data, which will affect health risk estimates and statistical power (Goldman et al. 2011, 2012), and validation of modeling results was rarely tested in environmental epidemiology. A joint indicator system should be established to assess the applicability of exposure and provide a theoretical basis for use in environmental epidemiology.
5 Conclusion Multiparameter statistical models have been used to simulate ambient ozone exposure, which improves the accuracy of exposure assessment in epidemiological studies. This review summarized the basic process of model development in terms of model selection, data preparation, determination of simulation scale, and model development and validation, introducing the underlying context and method for whole processes. Land use models are mature modeling techniques with low computational complexity, and they are easy to use to obtain satisfying model performance at small scales; however, they have limited capacities to capture temporal variations and can miss short-term and regional patterns. Comparatively, machine-learning algorithms are better at processing multiparameter data in larger research areas, which increases computational requirements. The models have their own characteristics, and the study purpose, study area, data availability, and computing resources should be considered 27
during the model selection and development process. In the future, further research based on multiple parameters and different simulation scales is needed.
Acknowledgments This work was funded by grants from the National Key Research and Development Program of China (Grant: 2017YFC0211706), the Special Foundation of Basic Science and Technology Resources Survey of the Ministry of Science and Technology of China (Grant: 2017FY101204), and the Beijing Natural Science Foundation (7172145).
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35
36
Table 1 Details of the studies included in this review Authors
Study location
(year)
Study
Temporal
Spatial
period
resolution
resolution
Model
Parameters
Atmospheric pressure, E
Precipitation, Relative hu 1
Zhan et al (2018)
Daily China
2015
maximum 8 h mean
0.1°×0.1
Random frost
°
model
duration, Temperature, W
Planetary boundary layer
anthropogenic emission i
normalized difference veg
(NDVI), road density, and
Satellite ozone measurem
air temperature, accumul
precipitation, downward s
flux, accumulated total e 2
Di
et
al
(2017)
2000-
8 h daily
1
2012
maximum
km
America
km×1
Neural network
planetary boundary layer
area fraction, precipitatio
water for the entire atmo
specific humidity at 2m, v
speed, medium cloud are
cloud area fraction, albed vegetation percentage Six 3
Average
Wang et al
metropolitan
1999-
(2015)
areas in
2013
America
two-week
50
concentratio
m
m×50
n
LUR with
traffic, industrial and por
universal
population density, land u
kriging
annual average of specifi
Traffic data, land use
4
Malmqvist
Umeå and
et al
Malmö,
(2014)
Sweden
LUR and 2012
Daily average
temporal
-
model
density,
altitude;
w
direction, global radiat
temperature, vertical tem
(2~8 m) and vertical tem (24 ~8 m)
LUR Adam-Po 5
upart et al (2014)
Quebec, Canada
8-hr midday 2005
concentratio n
1 km
km×1
mixed-effects model, Bayesian maximum
37
Temperature, precipitatio density and latitude;
entropy model and kriging method model 2012: Kerckhoff 6
s
et
al
Netherlands
2.28-3.15;
Average of
4.24-5.10;
the summer
9.4-9.20;
and annual
11.28-12.1
period
(2015)
50×50 m
LUR
Traffic intensity, length o
density residential land, u
4
7
8
9
Beelen et al (2009)
Wang et al (2016)
Europe
Los Angeles Basin, America
2001
Annual
1
average
km
2000-
two-week
2008
concentratio
Annual
1
(2017)
Germany
2015.4
average
km
Hourly and
30
monthly
m
(2018)
Metropolitan area of
density, meteorology, alt
LUR
CTM data, road networks
emissions, population den
n
2014.3-
0
LUR
the ocean.
4×4 km
Augsburg,
Son et al
Land use data, road
Average
Wolf et al
1
km×1
2011, 2014
Mexico
km×1
Local land use, building LUR
density,
household
de
coordinates, traffic variab m×30
Temperature, humidity, LUR
speed and hourly traffi elevation
CTM data, all roads, maj 1 1
De Hoogh, K.et
al
(2018)
Western Europe
2010
Annual
100
average
m×100 m
residential land cover, a LUR
trend, east-west trend model product, natural urban green
1
Huang
2
al (2017)
et
Nanjing, China
2013
Annual
100×100
average
m
38
LUR
Longitude and slope
Statistical
spatial-temporal
modeling
of
ambient
ozone
exposure
for
environmental epidemiology studies: a review
Highlights
A comprehensive review of statistical models for environmental exposure assessment was conducted. We summarized the basic process of research from model selection, data preparation, simulation scale determination, and model development and validation. Our review provides some insights on exposure measurement methods into the future of environmental epidemiology.
39
40
S0048-9697(19)34454-7 https://doi.org/10.1016/j.scitotenv.2019.134463 STOTEN 134463
To appear in:
Science of the Total Environment
Received Date: Revised Date: Accepted Date:
29 June 2019 28 August 2019 13 September 2019
Please cite this article as: R. Ma, J. Ban, Q. Wang, T. Li, Statistical spatial-temporal modeling of ambient ozone exposure for environmental epidemiology studies: a review, Science of the Total Environment (2019), doi: https:// doi.org/10.1016/j.scitotenv.2019.134463
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© 2019 Elsevier B.V. All rights reserved.
Statistical
spatial-temporal
modeling
of
ambient
ozone
exposure
for
environmental epidemiology studies: a review Authors: Runmei Ma1, Jie Ban1, Qing Wang1, Tiantian Li1*
1
National Institute of Environmental Health, Chinese Center for Disease Control and
Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China
Correspondence to: Tiantian Li, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, No. 7, Panjiayuan Nanli, Chaoyang District, Beijing 100021, China. Email: [email protected], Telephone: 8610-50930211
1
Abstract Background Studies have discovered the adverse health impacts of ambient ozone. Most epidemiological studies explore the relationship between ambient ozone and health effects based on fixed site monitoring data. Fine modeling of ground-level ozone exposure conducted by statistical models has great advantages for improving exposure accuracy and reducing exposure bias. However, there is no review summarizing such studies.
Objectives A review is presented to summarize the basic process of model development and to provide some suggestions for researchers.
Methods A search of PubMed, Web of Science and the Wanfang Database was performed for dates through July 1, 2019 to obtain relevant studies worldwide. We also examined the references of the articles of interest to ensure that as many articles as possible were included.
2
Results The land use regression model (LUR model), random forest model and artificial neural network model have been used in this field. We summarized these studies in terms of model selection, data preparation, simulation scale selection, and model establishment and validation. Multiparameters are a major feature of models. Parameters that influence the formation of ground-level ozone concentrations and parameters that have been extremely important in previous articles should be considered first. The process of model establishment and validation is essentially a process of continuously optimizing the model performance, but there are certain differences in the specific models.
Conclusion This review summarized the basic process of the statistical model for ambient ozone exposure. We gave the applicable conditions and application scope of different models and summarized the advantages and disadvantages of various models in ozone modeling research. In the future, research is still needed to explore this area based on its own research purposes and capabilities.
Key words: ambient ozone; statistical model; exposure assessment; environmental epidemiology 3
1 Introduction As a secondary air pollutant, ambient ozone is a concern due to its widespread pollution (Anenberg et al. 2010). The adverse health effects of ground-level ozone have been proven by many international studies, and the outcomes have showed varied effects, such as the mortality of all non-accidental causes, cardiovascular, respiratory and coronary diseases, and hypertension (Yin et al. 2017; Bell et al. 2014; Wong et al. 2008; Peng et al. 2013; Di et al. 2017b; Yang et al. 2017). The harmful effects of ozone have also been tested at more sophisticated levels by using, for example, glucose (Yang et al. 2018), blood pressure (Hunter et al. 2018) and inflammatory markers (Lee et al. 2018). Therefore, ambient ozone causes serious public health problems. Exposure assessments of ozone are key to exploring the relationship between ambient ozone and health.
For environmental epidemiological studies that focus on the health effects caused by ambient ozone, there is a great demand for the accurate assessment of ambient ozone exposure. For long-term exposure studies, high-resolution exposure data for historical periods were sparse, while for short-term exposure studies, many regions lack continuous daily ozone data from monitoring sites. Monitoring data have been commonly used in previous studies, but they are insufficient at the spatial and temporal scales and thus result in incomplete exposure assessment. Even when the monitoring data are complete, using the data to represent personal exposure results in 4
errors caused by the low precision of instruments or the gap between averaged individual exposure and the true level of air pollutants. Some studies further improved exposure accuracy by matching monitoring data based on home address, but Berkson error still exists due to differences between personal exposure level and averaged individual exposure measurements. To solve the above problems, some studies have explored the use of models to estimate ground-level ozone exposure at high spatial and temporal resolutions to try to avoid exposure assessment errors as much as possible. In recent years, focusing on the adverse health impacts of ozone and the accumulation of various data (satellite data, chemical transport modeling data, etc.), studies have used multiparameter statistical models to simulate ground-level ozone exposure, and the results have been applied to epidemiological studies; however, a review of these studies has not been conducted.
The objectives of this review are to discuss recent ground-level ozone statistical models for environmental epidemiology studies and to provide suggestions for future studies. We will provide a brief introduction of the models mostly used in this field. In this review, four factors will be considered: model selection, data preparation, simulation scale determination, and model development and validation. Applications and limitations will also be discussed.
2 Methods 5
To include a wide range of relevant studies, we searched important databases in English and Chinese, including PubMed, Web of Science and the Wanfang Database. The search was performed for dates through July 1, 2019 to obtain relative studies worldwide. The search words included were “ozone”, “O3”, “estimate”, “predict”, “forecast”, “spatiotemporal”, “spatial”, and “temporal”, which were searched in both English and Chinese. We also examined the references of the articles of interest to ensure that as many articles as possible were included.
The exclusion criteria for the review are as follows: 1) studies that do not mainly rely on mathematical statistics theories, such as chemical transport models (CTMs) and air quality models; 2) unspecified descriptions of the parameters and conducting process and unclear results; and 3) literature with repeated reports (Figure 1).
3 Results Based on a preliminary analysis of the literature, we found three types of models that have been widely used: the land use regression model (LUR model), the random forest model and the artificial neural network model. Detailed information of the studies included in the review is shown in Table 1.
3.1 Land use regression model 6
The LUR model is a multivariate regression model established by the least squares method to simulate the spatial distribution of the air pollutant concentrations in the study area. The model integrates land use data and traffic and population density data around the monitoring site into the geographic information system (GIS) to analyze the relationship between these parameters and the spatial distribution of pollutant concentrations to estimate the concentration at any location that does not have monitoring data (Wu et al. 2016). The variables are generated by setting an increasing buffer distance and selected according to the backward algorithm and the forward algorithm. Based on pollutant concentrations and geographic variables, a multivariate regression model was established by the least squares method.
LUR models were introduced in the air pollution modeling field in 1997 (Briggs et al. 1997), and they are normally used in regression mapping for ambient particulate matter, NOx or VOCs (Hoek et al. 2008). To the best of our knowledge, the earliest studies using LUR models to simulate ambient ozone exposure were conducted in 2009 (Beelen et al 2009). For the spatial scale, in some ways, LUR models mainly focus on a certain city or region, such as Quebec (Adam-Poupart et al. 2014) in Canada or metropolitan areas in the United States (Wang et al. 2015). However, some studies have modeled ground-level ozone exposure at a broader scale, such as Beelen et al. (2009), which was conducted in Europe. For the temporal scale, studies that have focused on long-term exposure are generally based on the annual mean value 7
(Beelen et al. 2009; Kerckhoffs et al. 2015; Wolf et al. 2017; De et al. 2018; Huang et al. 2017), and studies that have focused on short-term exposure have scattered hourly values (Son et al. 2018), 8-h averages (Adam-Poupart et al. 2014), daily means (Malmqvist et al. 2014) and two-week concentrations (Wang et al. 2015, 2016). Most LUR models applied in large metropolitan areas with high spatial resolutions (Hoke et al., 2008), such as the studies of the metropolitan areas of Mexico City, with a resolution of 30 m×30 m (Son et al. 2018), or Wang et al.(2015)’s research, which was based on six metropolitan areas in America and used a resolution of 50 m×50 m. According to the parameters used in various studies, commonly used parameters include land use term, traffic density, population density, emissions inventory and meteorological parameters. Most studies evaluated the performance of the model through the correlation coefficient (R2) or root mean square error (RMSE). The performances were generally good, with R2 values up to 0.92 (Augsburg, Germany, 1 km×1 km, hourly) (Wolf et al. 2017). The LUR model has been widely used in exposure assessments in Europe and North America, and the simulation results have been used in epidemiological studies (Holle et al. 2005).
3.2 Random forest model The random forest model is an algorithm based on a classification tree composed of a tree classifier {h (x, Θ k), k = 1,..}, where {Θ k} is an independent identical distribution random vector and each tree votes on x. The final results are obtained by 8
averaging the prediction results of the regression tree for multiple subsample sets through several rounds of training (Yang et al. 2014).
Random forest models have been widely used to simulate the spatial and temporal distributions of air pollutants (Hu et al. 2017; Zhan et al. 2017), but few studies have used random forest models in ambient ozone exposure simulations. Zhan et al. (2018) assessed the ambient ozone exposure intensity and duration in China in 2015. Meteorological data, elevation data, emissions inventories, land use data, vegetation indexes, road density data and population density data were prepared based on 0.1° × 0.1° resolution to model the daily maximum 8-h mean ozone concentration. The contribution of meteorological parameters was found to be 65%, and the contribution of evaporation, which is a comprehensive index of temperature and humidity and has an important influence on the formation and stability of ozone, was especially notable. The authors also compared results with the spatiotemporal kriging interpolation method, showing that the performance of the random forest model was slightly higher (the R2 of the random forest model was 0.69, while the R2 of the interpolation method was 0.68).
3.3 Artificial neural network model The artificial neural network is a nonlinear complex network system composed of a 9
large number of simple neurons. In the learning process of the neural network, the connection and topology of each neuron are constantly changing under the external stimulus until the network output gradually approaches the expected output. The model can be divided into a feedforward neural network and a feedback neural network. With continuous research, deep learning has become a new development in neural network research. Convolutional neural networks, as a type of deep feedforward neural network model, reduce the complexity of network models, have the ability to perform hierarchical learning and can accurately extract features (Chang, 2013; Lei, 2018).
Similar to the random forest model, the artificial neural network has many advantages, but few studies implementing this method are currently used for ground-level ozone simulation. Di et al. (2017a) established a convolutional neural network model that considered neighboring information through the introduction of the convolutional layer and used remote sensing ozone products, nitrogen oxide and sulfur dioxide data from both monitoring sites and satellite-based products, volatile organic compound (VOC) data from monitoring sites, chemical transport model outputs, meteorological data and land use variables to simulate near-surface ozone exposure based on a 1 km×1 km grid in the United States from 2000 to 2012. R2 reached 0.76, which indicates good performance. These predictive results were used in epidemiological studies that assessed the relationship between ozone and mortality in the U.S. from 10
2000 to 2012 (Di et al. 2017b).
4 Discussion We have reviewed the multiparameter statistical models used to simulate high-resolution ground-level ozone exposure. This review is of great significance for the development of future epidemiological studies and facilitates the use of accurate exposure assessment methods for subsequent studies. Furthermore, this review can provide a basic understanding from some perspectives for the introduction of policies and management measures to prevent and control the adverse effects of ambient ozone on human health. We will introduce the basic process of developing research that simulates ground-level ozone exposure using statistical models and compare the differences between studies to provide suggestions for future studies.
4.1 Model selection The most suitable model should be chosen after considering the study purpose, study area, data availability, and computing resources.
Currently, the LUR model is more widely used than the random forest and artificial neural network models, and the reasons are discussed as follows. First, the models
11
have undergone long-term development, and all aspects of the technology are mature, with fewer types of parameters, simpler operations and higher spatial resolutions. Furthermore, this model uses practical experience by setting parameter weights to establish models, which can avoid unreasonable results (Shi and Wang, 2016). However, it also has disadvantages, such as unstable modeling methods and poor spatial and temporal migration (Hoek et al. 2008). Different urban structures may result in different model performances for the same model. We found that the LUR model was more likely to be used in regions instead of whole countries because it has difficulty forming a prediction for long-term time series due to the search for high resolution. The LUR model has difficulty capturing small spatial changes in concentrations when the parameters of the study area are not very variable. When the change is too large, the modeled concentration tends to be higher than the true exposure, and the correlation is poor (Briggs et al. 2000).
The random forest and neural network models are black-box models that explore the relationship between ozone exposure and different variables and realize the rapid calculation of large-scale pollution simulations. The random forest model improves performance without drastically increasing computational complexity. In addition, it can obtain the order of parameter contributions, has high insensitivity to multivariate collinearity, and has high stability with missing and unbalanced data; thus, it can explain the influences of thousands of variables and does not need to be normalized 12
(Li, 2013). Furthermore, random forest models have certain advantages that can help to avoid overfitting: a model with more trees obtains a limited generalization error (Breiman et al. 2001; Shu X. 2013). However, it is not easy to explain the results during the process because of random forest models are black-box models. The artificial neural network has excellent performance and wide adaptability; however, it converges slowly and easily overfits high-dimensional features, and a large number of calculations results are required by the research team, which burdens the computing platform. The random forest model and artificial neural network are more advantageous than the regression model when handling multiparameter data, and they have prediction advantages for long-term time series and large study sites.
After obtaining a good understanding of the basic research situation, including the study purpose and the advantages and disadvantages of different models, researchers can choose the appropriate model according to their own needs. On this basis, parameters can be collected and prepared.
4.2 Data preparation 4.2.1 Monitoring data The research included in this review shows a very large difference in the area of the study region, from national scale to local scale, which means that the number of 13
monitoring sites varies widely. Instead of routine monitoring networks, some investigators set passive samplers on purpose or select stations for special design (Wolf et al. 2017; Malmqvist et al. 2014; Wang et al. 2015, 2016; Kerckhoffs et al. 2015). Purpose-designed monitoring sites normally consider population exposure. The researchers divide the sites into categories, such as regional background, urban background, traffic sites and industrial sites, or simply set monitoring sites at the location of the subjects. Wang et al. (2015) compared the difference between fixed sites and purpose-designed samplers: the R2 value for fixed locations ranged from 0.65 to 0.88, while for the predictions at home sites, the R2 value was between 0.60 and 0.91. Using purpose-designed monitoring or multiple sources of monitoring data can improve the ability to capture spatiotemporal variations in the model on a small scale, but it cannot adapt to the requirements for conducting large-scale studies.
In addition to differences in the number and type of monitoring network, the length and continuity in the time series of the monitoring data are also different. The metric used in the model should consider future applications. For health effects caused by long-term exposure, complete time series are more important than high temporal resolutions for the modeled results. For example, Wang et al. (2015) simulated two-week, ground-level ozone exposure from 1999–2013 in America. Due to the sample period, the weekly time frame for the monitoring data limited the prediction ability for 8-h maximum values that are commonly used, but investigators believed 14
that the two-week time scale can reduce the impact of meteorological factors and time autocorrelation. For studies focusing on acute health effects, high-temporal-resolution metrics are needed. LUR models often fail to form long-term time series in pursuit of a high spatial resolution, which is one of the issues that researchers should consider when choosing models. For studies that include long time scales and high resolutions, the machine-learning method is better.
4.2.2
Parameter selection and preparation
Multiparameters are a major feature used to simulate ambient ozone concentrations based on statistical models. They can extend the variable category and adjust the variable form. The explanatory ability of the model can be improved, and the influences of specific variables can be highlighted (Wu et al. 2016). The multiparameter trend for ambient ozone simulation is definite. Thus, parameters can be considered in several ways.
First, the mechanism of ground-level ozone formation should be considered. Ground-level ozone is a typical secondary atmospheric pollutant and is formed by a series of photochemical reactions of nitrogen oxides (NOx) and VOCs under ultraviolet light at a suitable temperature. A variety of typical factors directly affect the formation of ground-level ozone, including the concentration of precursor 15
materials, meteorological factors, and traffic-related factors affecting propagation. Socioeconomic, land use, vegetation coverage and other factors also have a certain impact on the formation and diffusion of the ground-level ozone concentration (Chen et al. 2017). More specifically, the parameters can include 1) meteorological parameters such as wind speed, wind direction, solar radiation, temperature, rainfall, and relative humidity, 2) geographic variables such as land use data, green space data, traffic data, residential and population density data, regional indicators, elevation, and distance from the sea, 3) emissions inventory parameters such as NO, NO2, organic carbon, NOx, VOCs, SO2 and particulate matter, and 4) monitoring site parameters such as NOx, sulfur oxides, VOCs and other pollutants at the ground monitoring site. Data products reflecting the formation, distribution and simulation of ground-level ozone concentrations, such as satellite data products provided by open platforms, such as the National Aeronautics and Space Administration (NASA) and CTM products from previous studies, were also used in models to improve performance.
Furthermore, after considering factors that influence the formation of ground-level ozone, new researchers can be inspired by previous research on the contribution of parameters. The contributions of variables vary by study. Zhan et al. (2018) found that meteorological variables have the highest relative importance, accounting for 65%, with the highest contributions being attributed to evaporation, temperature and humidity. Son et al. (2018) found that detailed emission patterns from local pollution 16
sources, coupled with wind field data (wind speed, wind direction and boundary layer height), are needed to improve current LUR models. Altitude (Beelen et al. 2009; Wang et al. 2016), road density, residential land (Beelen et al. 2009), green space and primary emission feature (traffic, population and impervious surfaces) (Wang et al. 2016) data are also contributors. Furthermore, Wang et al. (2016) and De et al. (2018) found that the use of CTMs results in models with improved performance. Some variable contributions were opposite in different studies. For example, Wang et al. (2015) found that smaller-scale GIS covariates, such as road network and population density, cannot represent the characteristics of ambient ozone well, which may lead to poor performance of the ozone simulation model. More kinds of parameters can be involved in the model, and the appropriate parameters can be selected during the model development process.
After parameter selection, the process of preparing key parameters varied in previous articles. The specific metrics of some variables were different based on data availability and the aims of the investigators. Taking traffic data as an example, a multicenter study conducted in Europe found that there was no high-resolution European database for roads, and traffic flow data were not consistently available in these countries; thus, road length was the only traffic variable in the model (Beelen et al. 2009). Some studies calculated conclusive variables to represent local traffic conditions, like traffic density. However, the definition of traffic density is not the 17
same in each study. Most LUR studies considered traffic density to be a long-term variable (Hoek et al. 2008). Adam-Poupart et al. (2014) defined traffic density as kilometers of road within a circular area with a 1-km radius, while Son et al. (2018) manually coded traffic density from the Google traffic website, and Kerckhoff et al. (2015) used traffic counts multiplied by 48 to obtain traffic intensities during daytime hours and then by 1.29 to calculate traffic intensities per 24-hour period. Some studies used multiple variables instead of conclusive variables, including road length, distance to nearby major roads and, within buffers, lengths of roads and truck routes and counts of intersections, to determine the specific relationship between traffic and ambient ozone (Wang et al. 2015). The quality of the raw traffic data was variable. Wang et al. (2015) used monitoring data from air quality system (AQS) monitors, which were purposefully placed away from roads, and studies using these data may have underestimated the effects of traffic indicators. Researcher considerations also impact data preparation. For example, Wolf et al. (2017) divided land use data into residential, industrial, built-up, urban green, forest, seminatural and water body areas, while Malmqvist et al. (2014) defined land use data as high-density residential, low-density residential, industrial, port, urban green, seminatural and forested areas. Four types of land use data were defined by Huang et al. (2017), including residential land, agricultural land, green spaces and water bodies.
The selection process mainly depends on the formation mechanism of ambient ozone 18
and previous studies or experience. Parameters commonly used in the model mainly include 1) meteorological variables such as temperature, humidity, evaporation, solar radiation, wind speed, boundary layer height, and precipitation, 2) precursor concentration, 3) geographical variables such as land use term, vegetation cover, and elevation, 4) emission inventory, 5) socioeconomic factors such as transportation, residential and population density, and 6) simulation products such as chemical transport model output (Table 1). Researchers can change the definitions of specific parameters based on their aims to obtain an optimal combination.
4.3 Simulation scale determination Due to the characteristics of data availability and study purposes, the time-scale simulation can be divided into continuous simulations and simulations for specific periods. Furthermore, the spatial and temporal resolutions can be divided into high and low resolutions. The high-spatiotemporal-resolution results can provide fine-concentration population exposure, which can improve accuracy and reduce conflicting factors, especially for studies focusing on acute health effects. However, this also increases the difficulty of data acquisition and processing and presents higher requirements for the hardware and software facilities of the research team. The low-spatiotemporal-resolution results can save time and manpower and obtain results more quickly. For long-term exposure studies that have low requirements for time-resolution results, researchers can choose metrics at the annual or monthly level 19
to improve effectiveness.
Based on study purpose and need, the model type can be chosen. For finer simulations, especially on a large study scale with complex trends, machine-learning methods performance better than the LUR model. Although studies that focused on ambient ozone exposure were rare, applications for other air pollutants proved superior in some contexts. Using PM2.5 as an example, Hu et al. (2017) used a random forest model in the United States in 2001 and obtained a simulation result of R2=0.8 based on a resolution of 1 km×1 km; Zhan et al. (2017) used a geographically weighted gradient lifting tree in China in 2014, and the result was obtained based on a spatial resolution of 0.5°×0.5° (R2=0.76). Di et al. (2016) used a convolutional neural network from 2000 to 2012 at a 1 km×1 km resolution and obtained a total R2 of 0.84; Li et al. (2017) used a neural network in China in 2015, with an R2 up to 0.88 based on a resolution of 0.1°×0.1°.
4.4 Model development and validation The process of model development varies based on model type. The land use model builds a linear regression model using a series of related variables to simulate ozone exposure. The random forest model can be regarded as a regression model when simulating ozone concentration by building relationships between independent and 20
dependent variables. The neural network freely learns any function form from the training data to simulate the ozone concentration. Di et al. (2017a) considered neighboring information during the modeling process through the introduction of a convolutional layer.
The processes of developing models based on regression algorithms and machine-learning algorithms are similar: model parameters and features are constantly adjusted to achieve optimal performance. Regression algorithms and machine-learning algorithms have similarities and differences in terms of model establishment and verification.
However, the development processes of the three models could be different. For the LUR model, parameter selection, which is mainly conducted through a supervised stepwise procedure, is important. Basically, a regression model is used for all potential predictors and the one with the highest explained variance is chosen. Then, researchers add further predictors step by step if the increase in adjusted R2 is >1%. Some researchers use a priori effect direction for each variable and require that the effect direction of the variable included must be the same as the a priori direction and that the effect direction of variables already in the model does not change. According to these researchers, not constraining a priori directions leads to models that make unrealistic predictions. A constraint was needed because there was considerable 21
collinearity between predictor variables (Beelen et al. 2009). Others had the opposite view: ozone, as a secondary pollutant, was involved in many reactions, and the effect directions were unclear (Malmqvist et al. 2014). Other researchers (Son et al. 2018) used the least absolute shrinkage and selection operator (LASSO) method, which aims to improve the prediction accuracy and interpretability of the model (Tibshirani, 1996) and the performance of the traditional LUR method. Developing a multivariate logistic regression model requires knowing the optimal number of prognostic factors to include. The LASSO method has a smaller mean square error (MSE) than conventional methods and handles the multicollinearity problem. Some researchers defined their own methods; for example, Wang et al (2015) developed a hierarchical spatiotemporal model to accommodate unique features and selected the best model based on the cross-validated R2 value. Furthermore, restrictions were proposed to remove variables in some situations. These restrictions aimed to improve the stability of the models (Wolf et al. 2017; Wang et al. 2015). For example, Wolf et al. set the following conditions: at least five sites must exhibit different values and the minimum or maximum values must lie within threefold of the 10th to 90th percentile range below or above the 10th and 90th percentiles. In this way, the selection of specific predictors that included mainly zeros or extreme outliers was prevented.
For the machine-learning method, like the integrated learning algorithm, parameter selection is mainly achieved by performing subsequent operations based on model 22
importance ordering of all parameters. The important parameters were number of variables included in every tree and the number of trees. Meanwhile, the deep learning algorithm has the ability to automatically learn features. The hidden layer number, hidden node number and active function should be set. These model parameters should be adjusted during the model development process depending on model performance, mainly reflecting R2, R, MSE, and RMSE, which are calculated during the validation process.
The main validation method is the cross-validation (CV) method, which can be divided into two types in these articles. The first type is leave-out-one cross validation (Wolf et al. 2017), which is the most common CV method (Geisser, 1974). In this field, the basic idea is that a monitoring station is excluded from the total number of sampling sites (K) to generate training datasets with K-1 sites, resulting in fitted models that simulate pollutant concentrations at the excluded sites (test set). This method is commonly used for the LUR model and has been criticized for overestimating predictive ability (Wang et al. 2013). The other method is V-fold CV (Son et al. 2018), where V=10, which means that the monitoring data are divided into 10 random splits, 9 for training the model and 1 for testing the model performance, until the best model performance is achieved, is the most common (Wang et al. 2015). Some researchers set the cut-off values at 75% and 25% (Beelen et al. 2009) or 80% and 20% (De et al. 2018). 23
In addition to model performance, the set of model parameters is also limited by the computing resources; for example, a higher tree number and number of steps for iteration would lead to better model performance in some contexts but also to a longer calculation time. The research team can set the parameters according to research purpose and ability and finally establish the optimal model at the current level.
4.5 Application of the simulation results A few studies modeled ground-level ozone exposure in Europe, North America and China, and some results have been used in epidemiological studies (Holle et al. 2005; Yang et al. 2018). The high resolution of modeled ambient ozone exposure data provided a chance, in some contexts, to effectively explore the relationship between ozone and human health.
Some studies achieved high model performance, but the robustness of the studies, i.e., what might happen when the model is extrapolated to other regions, should be considered. Some studies have found factors that influence the robustness of the models. The degree of urbanization and the distribution of the monitoring sites vary by region, so care should be taken when the data are extrapolated to other regions. For example, Wang et al. (2015) noted that models are limited when applied to areas 24
where monitoring networks were sparse; Adam-Poupart et al. (2014) found that estimates exhibit higher discrepancies from data values that are mostly from regions characterized by data scarcity. Kerckhoffs et al. (2015) found that, by excluding some observations, which increased the urbanization of sites, the robustness of the model remained good. Furthermore, Beelen et al. (2009) found that extrapolating far beyond the range of measured concentrations would lead to errors, with a possible solution being truncating the predicted concentrations to the highest measured concentration. To explore the relationship between ambient ozone and health more precisely, more high-resolution exposure assessments are needed, and care should be taken when assessment results are extrapolated from other places.
4.6 Prospects and limitations With the development of epidemiological studies, further studies are needed that focus on simulating ozone with high spatial and temporal resolutions. Complete time series and large coverage areas also provide convenience for the exposure assessments of populations on a fine scale. On one hand, the daily modeling data complemented the time series, especially provided historical exposure data; on the other hand, the high-spatial-resolution modeling results provide exposure data for areas without monitoring sites. Exposure data at high spatial and temporal resolutions improves exposure accuracy and reduces possible exposure assessment errors for both acute and chronic environmental epidemiological studies. The modeling results are 25
derived from statistical models based on a variety of parameters affecting ozone formation and elimination. The errors caused by relying solely on ozone monitoring instruments are reduced, as well as the errors due to the limited number and placement of monitoring stations. Furthermore, the modeling results with high spatial and temporal resolutions can better represent the spatiotemporal variability in the ambient ozone distribution and reduce the exposure error. Compared with developed countries, developing countries with shorter monitoring histories and rare distributions of monitoring air pollution stations have greater demand for these studies. Furthermore, considering the variance caused by the form of varied variables and the difficulties in comparing results, standard databases that are capable of multiple parameter requirements and shared platforms for researchers are inevitable.
Limitations still exist in this field. First, current studies that have focused on fine ambient ozone exposure modeling are limited and need to be continuously developed. Most studies have focused on low-pollution areas in developed countries, while few researchers have paid attention to highly polluted areas in developing countries, where monitoring stations are sparsely distributed and the monitoring history is not long. The demand for the fine-resolution assessment from epidemiological studies has not been completely met. Second, the validation of the modeling results remains a limitation. Availability and effectiveness should be tested not only by the model performance but also by the results of epidemiological studies that use modeling 26
results as exposure data. Few studies have evaluated error type and the amount of measurement error by comparing difference between true exposure and simulated exposure using different exposure data, which will affect health risk estimates and statistical power (Goldman et al. 2011, 2012), and validation of modeling results was rarely tested in environmental epidemiology. A joint indicator system should be established to assess the applicability of exposure and provide a theoretical basis for use in environmental epidemiology.
5 Conclusion Multiparameter statistical models have been used to simulate ambient ozone exposure, which improves the accuracy of exposure assessment in epidemiological studies. This review summarized the basic process of model development in terms of model selection, data preparation, determination of simulation scale, and model development and validation, introducing the underlying context and method for whole processes. Land use models are mature modeling techniques with low computational complexity, and they are easy to use to obtain satisfying model performance at small scales; however, they have limited capacities to capture temporal variations and can miss short-term and regional patterns. Comparatively, machine-learning algorithms are better at processing multiparameter data in larger research areas, which increases computational requirements. The models have their own characteristics, and the study purpose, study area, data availability, and computing resources should be considered 27
during the model selection and development process. In the future, further research based on multiple parameters and different simulation scales is needed.
Acknowledgments This work was funded by grants from the National Key Research and Development Program of China (Grant: 2017YFC0211706), the Special Foundation of Basic Science and Technology Resources Survey of the Ministry of Science and Technology of China (Grant: 2017FY101204), and the Beijing Natural Science Foundation (7172145).
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36
Table 1 Details of the studies included in this review Authors
Study location
(year)
Study
Temporal
Spatial
period
resolution
resolution
Model
Parameters
Atmospheric pressure, E
Precipitation, Relative hu 1
Zhan et al (2018)
Daily China
2015
maximum 8 h mean
0.1°×0.1
Random frost
°
model
duration, Temperature, W
Planetary boundary layer
anthropogenic emission i
normalized difference veg
(NDVI), road density, and
Satellite ozone measurem
air temperature, accumul
precipitation, downward s
flux, accumulated total e 2
Di
et
al
(2017)
2000-
8 h daily
1
2012
maximum
km
America
km×1
Neural network
planetary boundary layer
area fraction, precipitatio
water for the entire atmo
specific humidity at 2m, v
speed, medium cloud are
cloud area fraction, albed vegetation percentage Six 3
Average
Wang et al
metropolitan
1999-
(2015)
areas in
2013
America
two-week
50
concentratio
m
m×50
n
LUR with
traffic, industrial and por
universal
population density, land u
kriging
annual average of specifi
Traffic data, land use
4
Malmqvist
Umeå and
et al
Malmö,
(2014)
Sweden
LUR and 2012
Daily average
temporal
-
model
density,
altitude;
w
direction, global radiat
temperature, vertical tem
(2~8 m) and vertical tem (24 ~8 m)
LUR Adam-Po 5
upart et al (2014)
Quebec, Canada
8-hr midday 2005
concentratio n
1 km
km×1
mixed-effects model, Bayesian maximum
37
Temperature, precipitatio density and latitude;
entropy model and kriging method model 2012: Kerckhoff 6
s
et
al
Netherlands
2.28-3.15;
Average of
4.24-5.10;
the summer
9.4-9.20;
and annual
11.28-12.1
period
(2015)
50×50 m
LUR
Traffic intensity, length o
density residential land, u
4
7
8
9
Beelen et al (2009)
Wang et al (2016)
Europe
Los Angeles Basin, America
2001
Annual
1
average
km
2000-
two-week
2008
concentratio
Annual
1
(2017)
Germany
2015.4
average
km
Hourly and
30
monthly
m
(2018)
Metropolitan area of
density, meteorology, alt
LUR
CTM data, road networks
emissions, population den
n
2014.3-
0
LUR
the ocean.
4×4 km
Augsburg,
Son et al
Land use data, road
Average
Wolf et al
1
km×1
2011, 2014
Mexico
km×1
Local land use, building LUR
density,
household
de
coordinates, traffic variab m×30
Temperature, humidity, LUR
speed and hourly traffi elevation
CTM data, all roads, maj 1 1
De Hoogh, K.et
al
(2018)
Western Europe
2010
Annual
100
average
m×100 m
residential land cover, a LUR
trend, east-west trend model product, natural urban green
1
Huang
2
al (2017)
et
Nanjing, China
2013
Annual
100×100
average
m
38
LUR
Longitude and slope
Statistical
spatial-temporal
modeling
of
ambient
ozone
exposure
for
environmental epidemiology studies: a review
Highlights
A comprehensive review of statistical models for environmental exposure assessment was conducted. We summarized the basic process of research from model selection, data preparation, simulation scale determination, and model development and validation. Our review provides some insights on exposure measurement methods into the future of environmental epidemiology.
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