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Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing
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Research article
Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing Xiaoyu Qi a, Gang Mei a,∗, Salvatore Cuomo b, Chun Liu c, Nengxiong Xu a a
School of Engineering and Technology, China University of Geosciences (Beijing), China Department of Mathematics and Applications, University of Naples Federico II, Italy c School of Automation, Beijing University of Posts and Telecommunications, China b
a r t i c l e
i n f o
Article history: Received 23 June 2019 Revised 27 October 2019 Accepted 27 October 2019 Available online xxx Keywords: Data mining Air quality Meteorology Correlation Beijing
a b s t r a c t The air pollution caused by PM2.5, PM10, and O3 is an emerging problem that threatens public health, especially in China’s megacities. Meteorological factors have significant impacts on the dilution and diffusion of air pollutants which further affect the distribution and concentration of pollutants. In this paper, we analyze the relationships between air pollutant concentrations and meteorological conditions in Beijing from January 2017 to January 2018. We observe that: (1) the influence of a single meteorological factor on the concentration of pollutants is limited; (2) the temperature-wind speed combination, temperature-pressure combination, and humidity-wind speed combination are highly correlated with the concentration of pollutants, indicating that a variety of meteorological factors combine to affect the concentration of pollutants; and (3) different meteorological factors have different effects on the concentration of the same pollutant, while the same meteorological conditions have different effects on the concentration of different pollutants. Our findings can assist in predicting the air quality according to meteorological conditions while further improving the urban management performance. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Air pollution directly affects human health [1–3]. China has been experiencing serious air pollution problems in recent decades due to rapid industrialization and urbanization and increasing energy consumption [4]. The air pollution caused by PM2.5, PM10, and O3 is an emerging problem that threatens public health, especially in Chinese megacities [5]. With the rapid economic development in China, increasing attention has been given to environmental pollution. The Chinese government has deployed a number of pollution prevention and control action plans such as the “Air Pollution Prevention and Control Action Plan” which was aimed at continuously improving air quality while maintaining more blue skies for the masses [5]. At present, China has four regions with frequently occurring haze events: Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Sichuan Basin [6]. Understanding the role played by meteorological conditions and emissions control measures in recent years is critical for the development of further control measures in the future [7].
∗
Corresponding author. E-mail address: [email protected] (G. Mei).
https://doi.org/10.1016/j.iot.2019.100127 2542-6605/© 2019 Elsevier B.V. All rights reserved.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 1. Locations of the air quality monitoring stations and meteorological stations in Beijing.
Meteorological factors have significant impacts on the dilution and diffusion of air pollutants, and further affect the distribution and concentration of pollutants. For example, it was reported that the wind and rainfall could strongly affect the concentration of PM2.5, while relative humidity did not have similar effects [7,8]. Moreover, Ramsey et al [8] showed that under conditions of specific temperature and wind speed, the minimum concentration of O3 in a warmer and drier climate was higher than that in a cooler and wetter climate. It should also be noted that in most cases, the distribution and concentration of air pollutants are usually comprehensively influenced by several meteorological factors rather than only one factor. Much research work has been carried out to evaluate the effects of meteorological factors on the distribution and concentration of air pollutants by investigating their relationships [9,10]. For example, Kaminska [11] used the data mining method, random forests, to model the regression relationships between concentrations of the pollutants NO2 , NOx and PM2.5, with nine variables describing meteorological conditions, temporal conditions and traffic flow. Sun et al [12] analyzed the dynamic accumulation of PM2.5 using hourly concentrations between 18 January 2013 and 31 December 2016 in Beijing and Shanghai. Wang et al [13] emphasized the role of meteorology in pollution control, validated the effectiveness of PM2.5 control measures in China, and highlighted the importance of appropriate joint reduction of NOx and VOCs to simultaneously decrease O3 and PM2.5 for higher air quality. Li et al [14] examined the associations between Air Pollution Index (API) and meteorological factors during 2001 ∼ 2011 in Guangzhou, China, and used wavelet analyses to examine the relationships between API and several meteorological factors. Gui et al [15] used the newly released ground-level satellite-derived PM2.5 concentrations to characterize the long-term variations and trends of PM2.5 throughout the Eastern China to determine if the patterns were related to variations in pollutant emissions and meteorological parameters. Zhao et al [16] systematically investigated the influence of the midtropospheric westerly wind circulation on air quality by a regional weather research and forecasting chemical model, combined with the European Center for Medium Range Weather Forecasts reanalysis data Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 2. Rose map of annual average wind speed and direction in Beijing.
Fig. 3. Monthly average concentration accumulation of air pollutants in Beijing.
and ambient air quality measurements. Fang et al [17] investigated the multi-scale correlations between air quality and meteorology in the Guangdong-Hong Kong-Macau Greater Bay Area of China during 2015 ∼ 2017. In this paper, we obtain datasets describing six air pollutant concentrations and five meteorological factors in Beijing from January 2017 to January 2018 from the Harvard Dataverse [18]. We first investigate the relationships between the five meteorological factors and the six air pollutant concentrations. We further evaluate the effects of the meteorological conditions on air quality. It is of great value to select Beijing, the capital of China, as the representative research area to Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 4. Flowchart of the method for mining the correlation between meteorological conditions and air quality in Beijing.
study the relationship between the changes in meteorological factors and the concentration of air pollutants in recent years [19,20]. The main work in this paper is as follows. First, by qualitatively comparing the changes of meteorological conditions and air pollutant concentrations in the same time period, we preliminarily conclude that meteorological factors strongly affect the distribution and concentration of pollutants. Then, we quantitatively fit the relationships between (a) a single meteorological factor and (b) multiple meteorological factors and the air pollutant concentrations. The R-Square (R2 )) is used to evaluate the Goodness of Fit. The presented method is easy to conduct and the results are effective and reliable. The rest of this paper is organized as follows. Section 2 will briefly introduce the obtained dataset of meteorological conditions and air quality in Beijing along with the employed method for analyzing the dataset. Section 3 will present the analysis and mining of the correlations between the meteorological conditions and air quality. Finally, several conclusions will be drawn in Section 4. 2. Materials and methods 2.1. Datasets The public datasets of air pollutant concentrations and meteorological conditions in Beijing from January 1, 2017 to February 1, 2018 were obtained from the Harvard Dataverse [18]. The hourly average concentrations of six regulated air pollutants Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 5. Stratification diagram of monthly average air pollutant concentration in Beijing. The changing trends of different pollutants with time are different.
including O3 (μg/m3 ), SO2 (μg/m3 ), NO2 (μg/m3 ), PM2.5 (μg/m3 ), PM10 (μg/m3 ), and CO (mg/m3 ) were collected from 35 air quality monitoring stations. The hourly average meteorological parameters such as temperature (◦ ), pressure (hPa), relative humidity (%), wind speed (m/s), and wind direction (◦) were collected from 18 meteorological stations in the same period. Air quality data are provided by the Ministry of Environmental Protection of China. Meteorological data are first obtained by the National Oceanic and Atmospheric Administration (NOAA), and then processed by the Weather Research and Forecasting (WRF) model to generate grid meteorological data (21 x 31 points) with a grid spacing of 5 km. The availability of the obtained datasets is as follows: Dataset: Air pollution and meteorological data in Beijing 2017–2018. URL: https://doi.org/10.7910/DVN/USXCAK. For the obtained datasets, we first locate the air quality monitoring stations and meteorological stations on a map; see Fig. 1. We can see that most of the two categories of monitoring stations are distributed in urban areas, while several stations are scattered in the surrounding suburbs. Based on the dataset of the meteorological conditions in Beijing, we generate a rose map of annual average wind speed and direction in Beijing. As shown in Fig. 2, the northeast wind is the most common in Beijing, and the highest frequency of wind speed is 1.5 ∼ 2.5 m/s. For the obtained dataset of air quality in Beijing, the monthly average concentration accumulation of air pollutants chart of 2017–2018 is illustrated in Fig. 3. In general, the total pollutant concentration and the proportion of CO concentration are the highest in January 2017. The total pollutant concentration in other months does not change dramatically (within the change of 10 0 0 μg/m3 ). Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 6. Stratification diagram of monthly average meteorological conditions in Beijing. In summer, the air temperature and humidity are high, the pressure and average wind speed is low, and the southeast wind is the main factor. In winter, the temperature is low, the pressure is high, the humidity is low, the wind speed is obviously higher than that in summer, and the prevailing northerly wind is prevalent.
2.2. Our method for mining the correlation between meteorological conditions and air quality The workflow of the proposed method for mining the correlation between meteorological conditions and air quality in Beijing is illustrated in Fig. 4. For the obtained data, we first averaged the concentrations of various pollutants measured monthly by all air quality monitoring stations. We then visually expressed these values using a stratified line chart. As shown in Fig. 5, the changing trends of different pollutants with time are different. Similarly, we also calculate the monthly average of various meteorological factors obtained by all meteorological stations and use the stratified line chart in Fig. 6 to show that: (1) In summer, the air temperature and humidity are high, the pressure and average wind speed is low, and the southeast wind is the main factor. (2) In winter, the temperature is low, the pressure is high, the humidity is low, the wind speed is obviously higher than that in summer, and the prevailing northerly wind is prevalent. In summary, Beijing has a monsooninfluenced continental climate, characterized by hot, humid summers and cold, windy, and dry winters [21]. To further analyze the data and make the results more reliable and accurate, the monitoring data of Yanqing Air Quality Monitoring Station and Meteorological Station with the highest coinciding degree of geographical coordinates are selected Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 7. Plotting of the changes of the wind speed and air pollutant concentrations. It is intuitive to see that when the wind speed reaches its peak, the pollutant concentration is low.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 8. Fitted relationships between wind speed and air pollutant concentrations. Generally, the fitting performance of each group is not very high, and there is no obvious functional relationship between the groups. The influence of a single factor (i.e., the wind speed) on pollutant concentration is limited.
to represent the relationship between air pollutants concentration and climate change in Beijing. This group of specifically selected data can make the analysis more accurate. According to the hourly monitoring data of Yanqing Monitoring Station from January 1, 2017 to January 31, 2018, we calculate the daily average values of air pollutant concentrations and meteorological conditions. On this basis, we analyze the meteorological factors affecting the concentration of each pollutant by a single factor and multi-factors by fitting the functional relationships. The correlation coefficient is used to evaluate the influence of the meteorological factors on the concentration of air pollutants. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 9. Partially enlarged plotting of the changes of the wind speed and air pollutant concentrations. The wind speed is negatively correlated with the air pollutant concentrations, and in several days, the air pollutant concentrations are abnormally high.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 10. Plotting of the changes of the temperature and air pollutant concentrations. It is intuitive to see that temperature has no obvious relationship with PM2.5, PM10, NO2 and CO concentration but is positively correlated with the change of O3 and negatively correlated with the change of SO2 .
3. Results and discussion After calculating the average values of air pollutant concentrations and meteorological factors, first, by considering the effect of a single meteorological factor, the functional relationships between wind speed, temperature and other meteorological conditions and various pollutant concentrations are analyzed and fitted by Origin software. Then, by considering the Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 11. Fitted relationships between temperature and air pollutant concentrations. Specifically, the change in O3 concentration is strongly affected by temperature, and the fitting performance of the functional curve is relatively high. However, the fitting performance of the rest groups is not very high, and the influence of a single temperature factor on the concentration of pollutants is limited.
effects of two meteorological factors, a series of corresponding relationships are analyzed and fitted by combining two sets of factors. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 12. Functional relationship among other meteorological factors ((a) humidity and (b) pressure) and concentration of PM2.5. The fitting performance of the curve is not very high, a single meteorological factor has a slight effect on the concentration of pollutants.
Fig. 13. Functional relationship among temperature, pressure and concentration of PM2.5. The fitting performance of the functional relationship is high. Both the temperature and pressure comprehensively affect the concentration of PM2.5.
3.1. Relationships between a single factor and air pollutant concentrations 3.1.1. Relationships between wind speed and air pollutant concentrations The changes of the wind speed and air pollutant concentrations at the same time coordinate axis is plotted using the double Y-axis chart. As shown in Fig. 7, it is intuitive to see that when the wind speed reaches its peak, the pollutant concentration is low. When there is no wind or when the wind speed is low, the pollutant concentration is high. There is a negative correlation between the two. On this basis, the function fitting analysis is carried out, and the functional relationships are plotted in Fig. 8. Generally, the fitting performance of each group is not very high, and there is no obvious functional relationship between the groups. The influence of a single factor (i.e., the wind speed) on pollutant concentration is limited. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 14. Functional relationship among humidity, wind speed and concentration of PM2.5. The fitting performance of the functional relationship is high. Both the humidity and wind speed comprehensively affect the concentration of PM2.5.
Moreover, by enlarging the plot of the relationship between meteorological factors and air pollutant concentrations (Fig. 9), we can clearly observe that: (1) the wind speed is negatively correlated with the air pollutant concentrations; and (2) in several days, the air pollutant concentrations are abnormally high. More details are as follows: (1) In Fig. 9, it can be observed that the peaks of the fitted curves of air pollutant concentrations are correspondingly opposite to the valleys of the fitted curves of the wind speed. This result means that if the wind speed increases, then the concentration of air pollutants decrease and vice versa. (2) An abnormally high concentration of PM10 appeared on May 4, 2017. We analyzed the meteorological data of that day and found that the humidity on that day dropped 40% compared with the previous period while the wind speed remained at the level of 2 m/s. The temperature, wind speed and humidity gradually increased in the later period causing concentration of PM10 to decrease significantly. 3.1.2. Relationships between temperature and air pollutants The changes of the temperature and air pollutant concentrations at the same time coordinate axis are plotted using the double Y-axis chart. As shown in Fig. 10, it is intuitive to see that temperature has no obvious relationship with PM2.5, PM10, NO2 and CO concentration but is positively correlated with the change of O3 and negatively correlated with the change of SO2 . Based on the above approximately analysis, functional relationships are further fitted and illustrated in Fig. 11. Specifically, the change in O3 concentration is most affected by temperature, and the fitting performance of the functional curve is relatively high. However, the fitting performance of the rest groups is not very high, and the influence of a single temperature factor on the concentration of pollutants is limited. 3.1.3. Relationships of humidity, pressure and wind direction and air pollutants concentrations In addition to the above analysis of wind speed and temperature, the remaining meteorological factors, i.e., the humidity, pressure and wind direction also have an impact on the concentration of pollutants. Therefore, we also analyze the impact induced by each of the three meteorological factors. Because the fitting performance of the curve is not very high, a single meteorological factor has a slight effect on the concentration of pollutants. Here we simply present the fitting curves of Fig. 12 for reference. 3.2. Relationships between two factors and air pollutant concentrations Considering that multiple meteorological factors often comprehensively influence the dilution and diffusion of air pollutants which then further affect the distribution and concentration of pollutants, we use a graph model to fit the relationship We set one of the meteorological factors such as the temperature as the X-axis, another factor such as the wind speed as Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 15. Functional relationship among humidity, wind speed and concentration of PM10. The fitting performance of the functional relationship is high. Both the humidity and wind speed comprehensively affect the concentration of PM10.
Table 1 Correlations between the meteorological factors and air pollutant concentrations. The fitting performance of humidity-wind speed combination and temperature-pressure combination is high. Meteorological Factor
Air Pollutant
Factor 1 Wind Speed Wind Speed Wind Speed Temperature Temperature Humidity
PM2.5 0.82225 0.92153 0.21377 0.45822 0.93007 0.75327
Factor 2 Temperature Humidity Pressure Humidity Pressure Pressure
PM10 0.84938 0.90781 0.31038 0.28322 0.50641 0.6334
NO2 0.57903 0.69333 0.14049 0.31612 0.66233 0.22062
CO 0.70760 0.64325 0.15276 0.22105 0.69169 0.30015
O3 0.47774 0.66217 0.64466 0.43203 0.89458 0.09524
SO2 0.4179 0.86314 0.57559 0.35809 0.80259 0.40182
the Y-axis, and the concentration of various pollutants as the dependent variable expressed by the Z-axis. The performance measured with the correlation coefficient (R2 ) of all fittings is listed in Table 1. The above fitting results show that the fitting performance of humidity-wind speed combination and temperaturepressure combination is very high; see Figs. 13–16. Specifically, for the air pollutant PM2.5, the R2 of the fitting is 0.93 when the two factors are temperature and pressure (Fig. 13). For the air pollutant PM2.5, the R2 of the fitting is 0.92 when the two factors are humidity and wind speed (Fig. 14). For the air pollutant PM10, the R2 of the fitting is 0.91 when the two factors are humidity and wind speed (Fig. 15). For the air pollutant O3 , the R2 of the fitting is 0.89 when the two factors are temperature and pressure (Fig. 16). These results indicate that the temperature, pressure, humidity and wind speed are highly correlated with the concentrations of PM2.5, PM10, and O3 . In Figs. 14 and 15, the functional relationship of the changes in concentration of various pollutants under the influence of humidity and wind speed are plotted. Compared with the fitted curve under the influence of a single factor, the fitting performance of the functional relationship is much higher, and the fitting performance of PM2.5 and PM10 is even higher than 0.9. It can be seen that the diffusion and distribution of pollutants are complicated; both the humidity and wind speed comprehensively affect the distribution and concentration of pollutants. According to the results of the analyses, we can conclude that different meteorological factors have different effects on the concentration of the same pollutant while the same meteorological factors have different effects on the concentration of different pollutants. Moreover, the distribution and concentration of various pollutants are not affected by only a single factor but by multiple factors. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 16. Functional relationship among temperature, pressure and concentration of O3 . The fitting performance of the functional relationship is high. Both the temperature and pressure comprehensively affect the concentration of O3 .
4. Conclusions In this paper, we have analyzed the relationships between air pollutant concentrations and meteorological factors such as wind speed, temperature, and humidity in Beijing from January 2017 to January 2018. The presented method is easy to understand and the results are effective and reliable. We have found that: (1) the influence of a single meteorological factor on the concentration of pollutants is limited; (2) the temperature-wind speed combination, temperature-pressure combination, and humidity-wind speed combination are highly correlated with the concentration of pollutants, indicating that a variety of meteorological factors together affect the concentration of pollutants; and (3) different meteorological factors have different effects on the concentration of the same pollutant, while the same meteorological conditions have different effects on the concentration of different pollutants. Our findings are helpful to predict the air quality according to the meteorological factors. For example, by utilizing the fitted equations in Figs. 13–16, we can calculate the corresponding air quality values according to the monitored meteorological values in the case of missing air quality data. The prediction results can be used as a reference for people’s travel and life. Moreover, according to the drawn conclusions of our study, it is possible to give scientifically reasonable suggestions to the local environmental protection department, and further to improve the urban management performance. Declaration of Competing Interest None. Acknowledgments This work was jointly supported by the National Undergraduate Innovation and Entrepreneurship Training Program (X201911415064), the Natural Science Foundation of China (11602235 and 41772326), and the Fundamental Research Funds for the Central Universities (2652018091, 2652018107, and 2652018109). References [1] T. Xue, T. Zhu, Y. Zheng, Q. Zhang, Declines in mental health associated with air pollution and temperature variability in china, Nat. Commun. 10 (1) (2019) 2165, doi:10.1038/s41467- 019- 10196- y. [2] A. Caplin, M. Ghandehari, C. Lim, P. Glimcher, G. Thurston, Advancing environmental exposure assessment science to benefit society, Nat. Commun. 10 (1) (2019) 1236, doi:10.1038/s41467- 019- 09155- 4. [3] T. Vandyck, K. Keramidas, A. Kitous, J.V. Spadaro, R. Van Dingenen, M. Holland, B. Saveyn, Air quality co–benefits for human health and agriculture counterbalance costs to meet paris agreement pledges, Nat. Commun. 9 (1) (2018) 4939, doi:10.1038/s41467- 018- 06885- 9. [4] J. Hu, Q. Ying, Y. Wang, H. Zhang, Characterizing multi-pollutant air pollution in China: comparison of three air quality indices, Environ. Int. 84 (2015) 17–25, doi:10.1016/j.envint.2015.06.014.
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Research article
Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing Xiaoyu Qi a, Gang Mei a,∗, Salvatore Cuomo b, Chun Liu c, Nengxiong Xu a a
School of Engineering and Technology, China University of Geosciences (Beijing), China Department of Mathematics and Applications, University of Naples Federico II, Italy c School of Automation, Beijing University of Posts and Telecommunications, China b
a r t i c l e
i n f o
Article history: Received 23 June 2019 Revised 27 October 2019 Accepted 27 October 2019 Available online xxx Keywords: Data mining Air quality Meteorology Correlation Beijing
a b s t r a c t The air pollution caused by PM2.5, PM10, and O3 is an emerging problem that threatens public health, especially in China’s megacities. Meteorological factors have significant impacts on the dilution and diffusion of air pollutants which further affect the distribution and concentration of pollutants. In this paper, we analyze the relationships between air pollutant concentrations and meteorological conditions in Beijing from January 2017 to January 2018. We observe that: (1) the influence of a single meteorological factor on the concentration of pollutants is limited; (2) the temperature-wind speed combination, temperature-pressure combination, and humidity-wind speed combination are highly correlated with the concentration of pollutants, indicating that a variety of meteorological factors combine to affect the concentration of pollutants; and (3) different meteorological factors have different effects on the concentration of the same pollutant, while the same meteorological conditions have different effects on the concentration of different pollutants. Our findings can assist in predicting the air quality according to meteorological conditions while further improving the urban management performance. © 2019 Elsevier B.V. All rights reserved.
1. Introduction Air pollution directly affects human health [1–3]. China has been experiencing serious air pollution problems in recent decades due to rapid industrialization and urbanization and increasing energy consumption [4]. The air pollution caused by PM2.5, PM10, and O3 is an emerging problem that threatens public health, especially in Chinese megacities [5]. With the rapid economic development in China, increasing attention has been given to environmental pollution. The Chinese government has deployed a number of pollution prevention and control action plans such as the “Air Pollution Prevention and Control Action Plan” which was aimed at continuously improving air quality while maintaining more blue skies for the masses [5]. At present, China has four regions with frequently occurring haze events: Beijing-Tianjin-Hebei, the Yangtze River Delta, the Pearl River Delta, and the Sichuan Basin [6]. Understanding the role played by meteorological conditions and emissions control measures in recent years is critical for the development of further control measures in the future [7].
∗
Corresponding author. E-mail address: [email protected] (G. Mei).
https://doi.org/10.1016/j.iot.2019.100127 2542-6605/© 2019 Elsevier B.V. All rights reserved.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 1. Locations of the air quality monitoring stations and meteorological stations in Beijing.
Meteorological factors have significant impacts on the dilution and diffusion of air pollutants, and further affect the distribution and concentration of pollutants. For example, it was reported that the wind and rainfall could strongly affect the concentration of PM2.5, while relative humidity did not have similar effects [7,8]. Moreover, Ramsey et al [8] showed that under conditions of specific temperature and wind speed, the minimum concentration of O3 in a warmer and drier climate was higher than that in a cooler and wetter climate. It should also be noted that in most cases, the distribution and concentration of air pollutants are usually comprehensively influenced by several meteorological factors rather than only one factor. Much research work has been carried out to evaluate the effects of meteorological factors on the distribution and concentration of air pollutants by investigating their relationships [9,10]. For example, Kaminska [11] used the data mining method, random forests, to model the regression relationships between concentrations of the pollutants NO2 , NOx and PM2.5, with nine variables describing meteorological conditions, temporal conditions and traffic flow. Sun et al [12] analyzed the dynamic accumulation of PM2.5 using hourly concentrations between 18 January 2013 and 31 December 2016 in Beijing and Shanghai. Wang et al [13] emphasized the role of meteorology in pollution control, validated the effectiveness of PM2.5 control measures in China, and highlighted the importance of appropriate joint reduction of NOx and VOCs to simultaneously decrease O3 and PM2.5 for higher air quality. Li et al [14] examined the associations between Air Pollution Index (API) and meteorological factors during 2001 ∼ 2011 in Guangzhou, China, and used wavelet analyses to examine the relationships between API and several meteorological factors. Gui et al [15] used the newly released ground-level satellite-derived PM2.5 concentrations to characterize the long-term variations and trends of PM2.5 throughout the Eastern China to determine if the patterns were related to variations in pollutant emissions and meteorological parameters. Zhao et al [16] systematically investigated the influence of the midtropospheric westerly wind circulation on air quality by a regional weather research and forecasting chemical model, combined with the European Center for Medium Range Weather Forecasts reanalysis data Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 2. Rose map of annual average wind speed and direction in Beijing.
Fig. 3. Monthly average concentration accumulation of air pollutants in Beijing.
and ambient air quality measurements. Fang et al [17] investigated the multi-scale correlations between air quality and meteorology in the Guangdong-Hong Kong-Macau Greater Bay Area of China during 2015 ∼ 2017. In this paper, we obtain datasets describing six air pollutant concentrations and five meteorological factors in Beijing from January 2017 to January 2018 from the Harvard Dataverse [18]. We first investigate the relationships between the five meteorological factors and the six air pollutant concentrations. We further evaluate the effects of the meteorological conditions on air quality. It is of great value to select Beijing, the capital of China, as the representative research area to Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 4. Flowchart of the method for mining the correlation between meteorological conditions and air quality in Beijing.
study the relationship between the changes in meteorological factors and the concentration of air pollutants in recent years [19,20]. The main work in this paper is as follows. First, by qualitatively comparing the changes of meteorological conditions and air pollutant concentrations in the same time period, we preliminarily conclude that meteorological factors strongly affect the distribution and concentration of pollutants. Then, we quantitatively fit the relationships between (a) a single meteorological factor and (b) multiple meteorological factors and the air pollutant concentrations. The R-Square (R2 )) is used to evaluate the Goodness of Fit. The presented method is easy to conduct and the results are effective and reliable. The rest of this paper is organized as follows. Section 2 will briefly introduce the obtained dataset of meteorological conditions and air quality in Beijing along with the employed method for analyzing the dataset. Section 3 will present the analysis and mining of the correlations between the meteorological conditions and air quality. Finally, several conclusions will be drawn in Section 4. 2. Materials and methods 2.1. Datasets The public datasets of air pollutant concentrations and meteorological conditions in Beijing from January 1, 2017 to February 1, 2018 were obtained from the Harvard Dataverse [18]. The hourly average concentrations of six regulated air pollutants Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 5. Stratification diagram of monthly average air pollutant concentration in Beijing. The changing trends of different pollutants with time are different.
including O3 (μg/m3 ), SO2 (μg/m3 ), NO2 (μg/m3 ), PM2.5 (μg/m3 ), PM10 (μg/m3 ), and CO (mg/m3 ) were collected from 35 air quality monitoring stations. The hourly average meteorological parameters such as temperature (◦ ), pressure (hPa), relative humidity (%), wind speed (m/s), and wind direction (◦) were collected from 18 meteorological stations in the same period. Air quality data are provided by the Ministry of Environmental Protection of China. Meteorological data are first obtained by the National Oceanic and Atmospheric Administration (NOAA), and then processed by the Weather Research and Forecasting (WRF) model to generate grid meteorological data (21 x 31 points) with a grid spacing of 5 km. The availability of the obtained datasets is as follows: Dataset: Air pollution and meteorological data in Beijing 2017–2018. URL: https://doi.org/10.7910/DVN/USXCAK. For the obtained datasets, we first locate the air quality monitoring stations and meteorological stations on a map; see Fig. 1. We can see that most of the two categories of monitoring stations are distributed in urban areas, while several stations are scattered in the surrounding suburbs. Based on the dataset of the meteorological conditions in Beijing, we generate a rose map of annual average wind speed and direction in Beijing. As shown in Fig. 2, the northeast wind is the most common in Beijing, and the highest frequency of wind speed is 1.5 ∼ 2.5 m/s. For the obtained dataset of air quality in Beijing, the monthly average concentration accumulation of air pollutants chart of 2017–2018 is illustrated in Fig. 3. In general, the total pollutant concentration and the proportion of CO concentration are the highest in January 2017. The total pollutant concentration in other months does not change dramatically (within the change of 10 0 0 μg/m3 ). Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 6. Stratification diagram of monthly average meteorological conditions in Beijing. In summer, the air temperature and humidity are high, the pressure and average wind speed is low, and the southeast wind is the main factor. In winter, the temperature is low, the pressure is high, the humidity is low, the wind speed is obviously higher than that in summer, and the prevailing northerly wind is prevalent.
2.2. Our method for mining the correlation between meteorological conditions and air quality The workflow of the proposed method for mining the correlation between meteorological conditions and air quality in Beijing is illustrated in Fig. 4. For the obtained data, we first averaged the concentrations of various pollutants measured monthly by all air quality monitoring stations. We then visually expressed these values using a stratified line chart. As shown in Fig. 5, the changing trends of different pollutants with time are different. Similarly, we also calculate the monthly average of various meteorological factors obtained by all meteorological stations and use the stratified line chart in Fig. 6 to show that: (1) In summer, the air temperature and humidity are high, the pressure and average wind speed is low, and the southeast wind is the main factor. (2) In winter, the temperature is low, the pressure is high, the humidity is low, the wind speed is obviously higher than that in summer, and the prevailing northerly wind is prevalent. In summary, Beijing has a monsooninfluenced continental climate, characterized by hot, humid summers and cold, windy, and dry winters [21]. To further analyze the data and make the results more reliable and accurate, the monitoring data of Yanqing Air Quality Monitoring Station and Meteorological Station with the highest coinciding degree of geographical coordinates are selected Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 7. Plotting of the changes of the wind speed and air pollutant concentrations. It is intuitive to see that when the wind speed reaches its peak, the pollutant concentration is low.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 8. Fitted relationships between wind speed and air pollutant concentrations. Generally, the fitting performance of each group is not very high, and there is no obvious functional relationship between the groups. The influence of a single factor (i.e., the wind speed) on pollutant concentration is limited.
to represent the relationship between air pollutants concentration and climate change in Beijing. This group of specifically selected data can make the analysis more accurate. According to the hourly monitoring data of Yanqing Monitoring Station from January 1, 2017 to January 31, 2018, we calculate the daily average values of air pollutant concentrations and meteorological conditions. On this basis, we analyze the meteorological factors affecting the concentration of each pollutant by a single factor and multi-factors by fitting the functional relationships. The correlation coefficient is used to evaluate the influence of the meteorological factors on the concentration of air pollutants. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 9. Partially enlarged plotting of the changes of the wind speed and air pollutant concentrations. The wind speed is negatively correlated with the air pollutant concentrations, and in several days, the air pollutant concentrations are abnormally high.
Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 10. Plotting of the changes of the temperature and air pollutant concentrations. It is intuitive to see that temperature has no obvious relationship with PM2.5, PM10, NO2 and CO concentration but is positively correlated with the change of O3 and negatively correlated with the change of SO2 .
3. Results and discussion After calculating the average values of air pollutant concentrations and meteorological factors, first, by considering the effect of a single meteorological factor, the functional relationships between wind speed, temperature and other meteorological conditions and various pollutant concentrations are analyzed and fitted by Origin software. Then, by considering the Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 11. Fitted relationships between temperature and air pollutant concentrations. Specifically, the change in O3 concentration is strongly affected by temperature, and the fitting performance of the functional curve is relatively high. However, the fitting performance of the rest groups is not very high, and the influence of a single temperature factor on the concentration of pollutants is limited.
effects of two meteorological factors, a series of corresponding relationships are analyzed and fitted by combining two sets of factors. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 12. Functional relationship among other meteorological factors ((a) humidity and (b) pressure) and concentration of PM2.5. The fitting performance of the curve is not very high, a single meteorological factor has a slight effect on the concentration of pollutants.
Fig. 13. Functional relationship among temperature, pressure and concentration of PM2.5. The fitting performance of the functional relationship is high. Both the temperature and pressure comprehensively affect the concentration of PM2.5.
3.1. Relationships between a single factor and air pollutant concentrations 3.1.1. Relationships between wind speed and air pollutant concentrations The changes of the wind speed and air pollutant concentrations at the same time coordinate axis is plotted using the double Y-axis chart. As shown in Fig. 7, it is intuitive to see that when the wind speed reaches its peak, the pollutant concentration is low. When there is no wind or when the wind speed is low, the pollutant concentration is high. There is a negative correlation between the two. On this basis, the function fitting analysis is carried out, and the functional relationships are plotted in Fig. 8. Generally, the fitting performance of each group is not very high, and there is no obvious functional relationship between the groups. The influence of a single factor (i.e., the wind speed) on pollutant concentration is limited. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 14. Functional relationship among humidity, wind speed and concentration of PM2.5. The fitting performance of the functional relationship is high. Both the humidity and wind speed comprehensively affect the concentration of PM2.5.
Moreover, by enlarging the plot of the relationship between meteorological factors and air pollutant concentrations (Fig. 9), we can clearly observe that: (1) the wind speed is negatively correlated with the air pollutant concentrations; and (2) in several days, the air pollutant concentrations are abnormally high. More details are as follows: (1) In Fig. 9, it can be observed that the peaks of the fitted curves of air pollutant concentrations are correspondingly opposite to the valleys of the fitted curves of the wind speed. This result means that if the wind speed increases, then the concentration of air pollutants decrease and vice versa. (2) An abnormally high concentration of PM10 appeared on May 4, 2017. We analyzed the meteorological data of that day and found that the humidity on that day dropped 40% compared with the previous period while the wind speed remained at the level of 2 m/s. The temperature, wind speed and humidity gradually increased in the later period causing concentration of PM10 to decrease significantly. 3.1.2. Relationships between temperature and air pollutants The changes of the temperature and air pollutant concentrations at the same time coordinate axis are plotted using the double Y-axis chart. As shown in Fig. 10, it is intuitive to see that temperature has no obvious relationship with PM2.5, PM10, NO2 and CO concentration but is positively correlated with the change of O3 and negatively correlated with the change of SO2 . Based on the above approximately analysis, functional relationships are further fitted and illustrated in Fig. 11. Specifically, the change in O3 concentration is most affected by temperature, and the fitting performance of the functional curve is relatively high. However, the fitting performance of the rest groups is not very high, and the influence of a single temperature factor on the concentration of pollutants is limited. 3.1.3. Relationships of humidity, pressure and wind direction and air pollutants concentrations In addition to the above analysis of wind speed and temperature, the remaining meteorological factors, i.e., the humidity, pressure and wind direction also have an impact on the concentration of pollutants. Therefore, we also analyze the impact induced by each of the three meteorological factors. Because the fitting performance of the curve is not very high, a single meteorological factor has a slight effect on the concentration of pollutants. Here we simply present the fitting curves of Fig. 12 for reference. 3.2. Relationships between two factors and air pollutant concentrations Considering that multiple meteorological factors often comprehensively influence the dilution and diffusion of air pollutants which then further affect the distribution and concentration of pollutants, we use a graph model to fit the relationship We set one of the meteorological factors such as the temperature as the X-axis, another factor such as the wind speed as Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 15. Functional relationship among humidity, wind speed and concentration of PM10. The fitting performance of the functional relationship is high. Both the humidity and wind speed comprehensively affect the concentration of PM10.
Table 1 Correlations between the meteorological factors and air pollutant concentrations. The fitting performance of humidity-wind speed combination and temperature-pressure combination is high. Meteorological Factor
Air Pollutant
Factor 1 Wind Speed Wind Speed Wind Speed Temperature Temperature Humidity
PM2.5 0.82225 0.92153 0.21377 0.45822 0.93007 0.75327
Factor 2 Temperature Humidity Pressure Humidity Pressure Pressure
PM10 0.84938 0.90781 0.31038 0.28322 0.50641 0.6334
NO2 0.57903 0.69333 0.14049 0.31612 0.66233 0.22062
CO 0.70760 0.64325 0.15276 0.22105 0.69169 0.30015
O3 0.47774 0.66217 0.64466 0.43203 0.89458 0.09524
SO2 0.4179 0.86314 0.57559 0.35809 0.80259 0.40182
the Y-axis, and the concentration of various pollutants as the dependent variable expressed by the Z-axis. The performance measured with the correlation coefficient (R2 ) of all fittings is listed in Table 1. The above fitting results show that the fitting performance of humidity-wind speed combination and temperaturepressure combination is very high; see Figs. 13–16. Specifically, for the air pollutant PM2.5, the R2 of the fitting is 0.93 when the two factors are temperature and pressure (Fig. 13). For the air pollutant PM2.5, the R2 of the fitting is 0.92 when the two factors are humidity and wind speed (Fig. 14). For the air pollutant PM10, the R2 of the fitting is 0.91 when the two factors are humidity and wind speed (Fig. 15). For the air pollutant O3 , the R2 of the fitting is 0.89 when the two factors are temperature and pressure (Fig. 16). These results indicate that the temperature, pressure, humidity and wind speed are highly correlated with the concentrations of PM2.5, PM10, and O3 . In Figs. 14 and 15, the functional relationship of the changes in concentration of various pollutants under the influence of humidity and wind speed are plotted. Compared with the fitted curve under the influence of a single factor, the fitting performance of the functional relationship is much higher, and the fitting performance of PM2.5 and PM10 is even higher than 0.9. It can be seen that the diffusion and distribution of pollutants are complicated; both the humidity and wind speed comprehensively affect the distribution and concentration of pollutants. According to the results of the analyses, we can conclude that different meteorological factors have different effects on the concentration of the same pollutant while the same meteorological factors have different effects on the concentration of different pollutants. Moreover, the distribution and concentration of various pollutants are not affected by only a single factor but by multiple factors. Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127
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Fig. 16. Functional relationship among temperature, pressure and concentration of O3 . The fitting performance of the functional relationship is high. Both the temperature and pressure comprehensively affect the concentration of O3 .
4. Conclusions In this paper, we have analyzed the relationships between air pollutant concentrations and meteorological factors such as wind speed, temperature, and humidity in Beijing from January 2017 to January 2018. The presented method is easy to understand and the results are effective and reliable. We have found that: (1) the influence of a single meteorological factor on the concentration of pollutants is limited; (2) the temperature-wind speed combination, temperature-pressure combination, and humidity-wind speed combination are highly correlated with the concentration of pollutants, indicating that a variety of meteorological factors together affect the concentration of pollutants; and (3) different meteorological factors have different effects on the concentration of the same pollutant, while the same meteorological conditions have different effects on the concentration of different pollutants. Our findings are helpful to predict the air quality according to the meteorological factors. For example, by utilizing the fitted equations in Figs. 13–16, we can calculate the corresponding air quality values according to the monitored meteorological values in the case of missing air quality data. The prediction results can be used as a reference for people’s travel and life. Moreover, according to the drawn conclusions of our study, it is possible to give scientifically reasonable suggestions to the local environmental protection department, and further to improve the urban management performance. Declaration of Competing Interest None. Acknowledgments This work was jointly supported by the National Undergraduate Innovation and Entrepreneurship Training Program (X201911415064), the Natural Science Foundation of China (11602235 and 41772326), and the Fundamental Research Funds for the Central Universities (2652018091, 2652018107, and 2652018109). References [1] T. Xue, T. Zhu, Y. Zheng, Q. Zhang, Declines in mental health associated with air pollution and temperature variability in china, Nat. Commun. 10 (1) (2019) 2165, doi:10.1038/s41467- 019- 10196- y. [2] A. Caplin, M. Ghandehari, C. Lim, P. Glimcher, G. Thurston, Advancing environmental exposure assessment science to benefit society, Nat. Commun. 10 (1) (2019) 1236, doi:10.1038/s41467- 019- 09155- 4. [3] T. Vandyck, K. Keramidas, A. Kitous, J.V. Spadaro, R. Van Dingenen, M. Holland, B. Saveyn, Air quality co–benefits for human health and agriculture counterbalance costs to meet paris agreement pledges, Nat. Commun. 9 (1) (2018) 4939, doi:10.1038/s41467- 018- 06885- 9. [4] J. Hu, Q. Ying, Y. Wang, H. Zhang, Characterizing multi-pollutant air pollution in China: comparison of three air quality indices, Environ. Int. 84 (2015) 17–25, doi:10.1016/j.envint.2015.06.014.
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Please cite this article as: X. Qi, G. Mei and S. Cuomo et al., Data analysis and mining of the correlations between meteorological conditions and air quality: A case study in Beijing, Internet of Things, https://doi.org/10.1016/j.iot.2019.100127