Exploring the economy-wide effects of agriculture on air quality and health: Evidence from Europe

Science of the Total Environment 663 (2019) 889–900

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Exploring the economy-wide effects of agriculture on air quality and health: Evidence from Europe Elias Giannakis a,⁎, Jonilda Kushta a, Despina Giannadaki a, George K. Georgiou a, Adriana Bruggeman a, Jos Lelieveld a,b a b

The Cyprus Institute, Energy Environment and Water Research Center, 2121 Nicosia, Cyprus Max Planck Institute for Chemistry, Atmospheric Chemistry Department, 55128 Mainz, Germany

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Economic models and WRF-Chem can model agricultural growth effect on air quality. • Agriculture has highest NH3 and NMVOC emissions per change in economic output in EU. • Shipping has highest SOx, NOx and PM emissions per change in economic output in EU. • Increase in agricultural emissions affects southeastern EU countries most strongly. • 20% agricultural output increase results in 24% increase in excess mortality in EU.

a r t i c l e

i n f o

Article history: Received 30 November 2018 Received in revised form 29 January 2019 Accepted 30 January 2019 Available online 31 January 2019 Editor: Pavlos Kassomenos Keywords: Environmentally-extended input-output models WRF-Chem PM2.5 in Europe Outdoor pollution Premature mortality Agricultural emissions

a b s t r a c t Agricultural emissions strongly contribute to fine particulate matter pollution (PM2.5) and associated effects on human health. Environmentally-extended input-output models and a regional atmospheric chemistry model (WRF-Chem) were combined to conduct an economy-wide assessment of air pollution and pre-mature mortality in the European Union (EU), associated with a 20% increase in the final demand for the output of the agricultural sector. Model results revealed significant differences in air pollution originating from agricultural growth across the 28 EU countries (EU-28). The highest impact of agricultural growth on PM2.5 concentrations occur over the Northern Balkan countries (Bulgaria and Romania) and northern Italy. However, the highest excess mortality rates in the EU-28 due to changes in emissions and enhanced PM2.5 concentrations are observed in Malta, Greece, Spain and Cyprus. The least affected countries are mostly located in the northern part of Europe, with the exception of the Scandinavian Countries, which have relatively good air quality under current conditions. Our integrated modelling framework results highlight the importance of capturing both the direct and indirect air pollution emissions of economic sectors via upstream supply chains and underscore the non-linear response of surface PM2.5 levels and their health impacts to emission fluxes. © 2019 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Corresponding author at: 20 Konstantinou Kavafi Street, 2121 Aglantzia, Nicosia, Cyprus. E-mail address: [email protected] (E. Giannakis).

https://doi.org/10.1016/j.scitotenv.2019.01.410 0048-9697/© 2019 Elsevier B.V. All rights reserved.

Air pollution from fine particulate matter (diameter b2.5 μm, hereafter PM2.5) has been associated with many health impacts including

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chronic obstructive pulmonary disease, ischemic heart disease, cerebrovascular disease (stroke), acute lower respiratory infection and lung cancer (Burnett et al., 2014). Exposure to PM2.5 and ozone is considered responsible for 3.3 million excess deaths annually worldwide, of which 381,000 premature deaths per year across Europe (Lelieveld et al., 2015). Based on updated epidemiological data, the numbers are growing, leading to an estimated 4.55 million (95% CI 3.41 million to 5.56 million) excess deaths attributable to air pollution in 2015 (Lelieveld et al., 2018). Relaxing the assumptions by examining exposure and risk information restricted to cohort studies of outdoor air pollution, Burnett et al. (2018) predicted even 8.9 million [95% confidence interval (CI): 7.5–10.3] excess deaths in 2015. These estimates are twofold the risk function used in the global burden of disease studies (4 million deaths, GBD, 2015) and World Health Organization estimates (4.2 million deaths, WHO, 2014), suggesting that mortality attributed to outdoor particulate air pollution is larger than previously assumed. Several studies have shown that agricultural emissions make the largest relative contribution to PM2.5 in Europe, thus enhancing excess mortality rates (Lelieveld et al., 2015; Lee et al., 2015; Bauer et al., 2016); for example, in Germany the contribution by agriculture to PM2.5 and associated mortality is 45% (Lelieveld et al., 2015). In Europe, much progress has been made in regulating the emissions of many air pollutants resulting in improved air quality across the continent; e.g., from 2008 to 2015 sulphur oxides (SOx), PM2.5, nitrogen oxides (NOx) and carbon monoxide (CO) emissions fell by 50%, 28%, 25%, and 23%, respectively (Eurostat, 2018a). However, not all economic sectors have had a satisfactory performance in terms of regulating air pollutant emissions. Agricultural activities continue to emit significant amount of air pollutants; e.g., emissions of NH3, which is the dominant air pollutant from animal husbandry and fertilizer use (around 96% of atmospheric NH3 emissions in the European Union (EU-28) originate from agriculture), having remained stable from 2008 to 2015 (Eurostat, 2018a). OECD (2016) projections of sectoral economic activities from 2015 to 2060 indicate a strong increase of NH3 and NOx emissions in the coming decades due to the projected increase in the demand for agricultural products. Ammonia plays a central role in the formation of secondary particulate matter through the formation of ammonium sulphate and ammonium nitrate (Behera and Sharma, 2010). The regulation of agricultural ammonia emissions is the most effective control strategy in reducing PM2.5 in Europe (Aksoyoglu et al., 2011; Megaritis et al., 2013). Erisman and Schaap (2004) showed that secondary fine PM concentrations can be effectively reduced if NH3 emissions are comparably decreased as those of SO2 and NOx. Giannadaki et al. (2018) found that a 50% reduction in agricultural emissions could reduce mortality by 18% (from 173 to 142 thousand deaths in 2010) with an annual economic benefit of 89 billion US$, while a theoretical complete phase-out of agricultural emissions could prevent 140 thousand deaths per year with an associated economic benefit of about 407 billion US$ per year. Although the main reason behind the reduction of the emission of air pollutants in Europe is the policy development and regulation (Vestreng et al., 2007), this is not yet the case for agriculture. Factors behind the variable and slow response of European countries to air pollution abatement policies in agriculture include high costs, lack of robust evidence on their effectiveness, legislative delays and others (Collins et al., 2016; Oenema et al., 2009). Several studies reported in the literature have explored the impact of reductions in agricultural emissions on air quality and human health (Megaritis et al., 2013; Pozzer et al., 2017). However, little is known about the socioeconomic drivers of air pollution triggered by the consumption perspective (Ou et al., 2017). While the number of air pollution sources is relatively small, the number of intermediate and final consumers that drive the activity is relatively large (Moran and Kanemoto, 2016). Tracking the embodied emissions involved in intermediate production flows along production supply chains and in final demand flows can provide a better understanding of how air pollutants

are triggered through economic activity (Peters, 2008; Huo et al., 2014). Environmentally-extended input-output analysis (EE-IOA) models, which link sectoral air pollution emissions with the financial transactions of the economic sectors, have been widely used to analyse the drivers of environmental changes associated with air pollution emissions (Ou et al., 2017; Huo et al., 2014), thus estimating the economywide environmental impact of consumption (Wiedmann et al., 2015). For studies focused on specific regions, limited area atmospheric chemistry models can provide accurate information on ground-level concentration of pollutants, a significant parameter in mortality estimates attributable to outdoor air pollution (Tuccella et al., 2012; Im et al., 2014a, 2014b; Kushta et al., 2018). An additional feature considered important in atmospheric modelling is the integrated treatment of meteorological and chemical mechanisms through an online modelling system that allows for the simulation of the feedback mechanisms between pollution and meteorological processes (Baklanov et al., 2014, Grell and Baklanov, 2011). In this study the Weather Research and Forecast model coupled with chemistry (WRF/Chem version 3.9.1.1) is used (Fast et al., 2006; Grell et al., 2005). The modelling system comprises an air quality and a meteorological component. The components are fully consistent as they apply the same atmospheric transport scheme (mass and scalar preserving), physical schemes for subgrid-scale transport and the same spatial and temporal configuration. WRF/Chem has been evaluated over Europe regarding several pollutants. Mar et al. (2016) showed that WRF-Chem can capture spatial distribution of ozone, but overestimations can occur based on the mechanism deployed, pointing at the Regional Acid Deposition Model (RADM) as the more accurate. Regarding fine particulate matter, studies have shown that despite discrepancies in PM2.5 sub-components the model can be considered acceptable for standard modelling applications (Tuccella et al., 2012; Berger et al., 2016). Specifically for mortality estimates, Kushta et al. (2018) evaluated the model against AIRBASE measurements and showed that WRF/Chem provides a comprehensive tool for the representation of mean annual PM2.5 levels over Europe. This paper aims to contribute to the literature along two main directions. First, it aims to assess the direct and indirect contribution of production sectors of the EU-28 economy to the emissions of air pollutants, accounting for all monetary inter-industry transactions, thus analysing the channels through which the environmental burdens of economic activities are transmitted throughout the European economy. Given the importance of agriculture as a source of air pollution, a detailed analysis of the contribution of the agricultural sector is of major relevance. To the best of our knowledge, this is the first contribution that combines EE-IOA models with atmospheric chemistry models throughout the EU28, thus allowing a holistic assessment of the economy-wide impact of agricultural activities on air quality and public health. 2. Methodology In this study, we performed an economy-wide assessment of air pollution and mortality in the EU-28, associated with a 20% increase in the final demand for the output of the agricultural sector (hereafter Agri20 scenario). To do so, economy-wide emission factors for agriculture derived from EE-IOA models of the EU-28 countries were used to adjust the spatially and temporally aggregated reported emission data of the region. The original and modified emission inventories feed into a regional atmospheric chemistry model that quantifies the impact of the change in the final demand of agriculture on the PM2.5 levels and mortality estimates in the region. 2.1. Environmentally-extended input-output analysis 2.1.1. Input-output analysis Input-output analysis (IOA) is a quantitative technique for studying the interdependence of production sectors in an economy over a stated time period (Miller and Blair, 2009), and it has been extensively applied

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for policy impact evaluation, structural and technical change analysis (Giannakis and Bruggeman, 2017). We transformed the EU-28 and the national IOA tables (Eurostat, 2017) to a system of linear equations accounting for the way in which the output of each sector i is distributed through sales to other sectors (intermediate demand) and final demand as follows (Miller and Blair, 2009): xi ¼

n X

xij þ yi

ð1Þ

j¼1

where xi describes the total output of sector i (i = 1, … , n) in Euro; xij describes the inter-industry sales by sector i to all sectors j (j = 1, … , n) in Euro; yi describes the final demand for sector i's product in Euro. The matrix A (nxn) of technical coefficients aij denotes the total output from sector i that is required to produce one unit of output in sector j as follows: aij ¼ xij =x j

ð2Þ

The technical coefficients aij represent the production technology and are assumed to be fixed within a short time frame. By replacing the intermediate sales xij from Eq. (2), Eq. (1) can be written in matrix notation as follows: X ¼ AX þ Y

ð3Þ

Solving Eq. (3) for X, we obtained the basic equation of IOA: X ¼ ðI−AÞ−1 Y

ð4Þ

where (I − A)−1 = L is known as the Leontief inverse or the total (direct and indirect) requirements matrix (Leontief, 1966). The L matrix (lij) quantifies all the infinite series of round-by-round direct and indirect effects exerted by a change in the final demand of a sector (ΔY) on the economic output of each sector (ΔΧ) (Miller and Blair, 2009). The output multiplier for a particular sector j equals to the column sums of P the L matrix, i.e., ni¼1 lij . 2.1.2. Environmentally-extended input-output analysis Over the past decades, the IOA framework has been extended to encompass the interaction of the economy with the environment by adding pollution intensity vectors for each sector (Minx et al., 2009; Cortés-Borda et al., 2015). The basic IOA model relationship Eq. (4) can be applied to assess the direct and indirect air pollutant emissions as indicated in Eq. (7). The extension of an IOA model to EE-IOA model involves the use of an exogenous vector of direct air pollutant emissions, here denoted as D = [dki], that is, the amount of k pollutants emitted (eki) to produce one unit monetary output of each sector i as follows (Miller and Blair, 2009): dki ¼

eki xi

ð5Þ

The total amount of pollutant emissions (E) driven by final demands (Y) for n sectors can be calculated as follows: E ¼ XD

ð6Þ

  E ¼ ½I−A−1 Y D

ð7Þ

2.1.3. Data and application The EU-28 and the national symmetric IOA tables for the year 2010, expressed at basic prices, were derived from the Eurostat database (Eurostat, 2017). The World Input-Output Database (WIOD) (Timmer et al., 2015) was used to derive IOA symmetric tables for Luxemburg,

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Malta, Poland and Portugal. The WIOD tables were converted to Euro currency by using the average exchange rate of the European Central Bank for 2010, that is, 1.326 (1 Euro = 1.326 USD). The initial scheme of 65 sectors of economic activity (56 sectors for the WIOD tables) was aggregated into 16 economic sectors (Table A1). The air pollution emissions by sector of economic activity used in the multiplier analysis include: Sulphur oxides (SOx), Nitrogen oxides (NOx), Ammonia (NH3), Non-methane volatile organic compounds (NMVOC), Carbon monoxide (CO), Fine particulate matter with a diameter b2.5 μm (PM2.5) and Coarse particles with a diameter b10 μm (PM10) (Eurostat, 2018a). In this study, we explore the direct and indirect impact of the increase in the final demand for the output of agriculture by 20% (Agri20 scenario), in the air pollutant emissions from agriculture, considering that in the past twenty years, the economic output of the EU-28 was growing with an average annual rate of nearly 2% in real terms, while the real growth of the European agricultural sector was around 1% at an annual basis (Eurostat, 2018b). Sources of uncertainty typically associated with EE-IOA are: (a) sampling and reporting errors in basic source data; (b) the assumption made in single-country IOA models that imported goods and services are being produced with the same technology as the domestic technology; (c) the assumption of proportionality between monetary and physical flows; (d) the aggregation of input-output data over different products supplied by one industry (Lenzen et al., 2004; Dias et al., 2014). Despite these criticisms, EE-IOA provides a valuable tool to explore structural change including: (a) changes in the size of economic sectors; (b) technological change in different sectors; (c) availability of different environmental resources and/or pollutant factors (Hubacek et al., 2009). 2.2. Atmospheric chemistry model The WRF/Chem atmospheric model offers the option of several different chemistry, aerosol and physical parameterizations that can be user-selected. The sensitivity tests performed in the framework of this analysis utilize the following physical and chemical options: Gas phase chemistry is simulated with the second generation Regional Acid Deposition Model (RADM2) mechanism (Stockwell et al., 1990) while the aerosol processes are included in the Modal Aerosol Dynamics Model for Europe (MADE) (Ackermann et al., 1998) for inorganic species, and the Secondary Organic Aerosol Model (SORGAM) for secondary organic aerosols (SOA) (Schell et al., 2001). The options for the physical parameterizations include the Morrison scheme (Morrison et al., 2005) for the representation of the microphysical processes, the MM5 similarity surface layer scheme (Zhang and Anthes, 1982), the Yonsei University (YSU) Planetary Boundary Layer scheme (Hong et al., 2006), the Noah Land Surface Model (Tewari et al., 2004), the Grell 3D Ensemble Scheme for cumulus parametrization (Grell and Devenyi, 2002) and the Rapid Radiative Transfer Model for both short and long wave radiation budget (RRTMG) scheme (Iacono et al., 2008). The photolytic reactions are treated with the chemical parametrization as in the Fast-J photolysis scheme (Wild et al., 2000). We simulated a year-long period with meteorological forcing from the National Centers for Environmental Prediction (NCEP) global forecast system (GFS) and chemical conditions from global simulations with MOZART-4 (Model for Ozone And Related chemical Tracers version 4) model (Emmons et al., 2010). Emissions were calculated from the global emission dataset EDGAR-HTAP v2 (Janssens-Maenhout et al., 2012). The EDGAR-HTAP dataset has a resolution of 0.1° × 0.1° (latitude/longitude) and provides annual anthropogenic emissions of NOx, SOx, NMVOC, CO, NH3, PM2.5 and PM10 covering 11 source sectors (SNAP categories) plus emissions from ships and volcanoes (SOx) for the reference year 2010. The NMVOCs of the EDGAR-HTAP v2 dataset are lumped into reactive compound categories, and the speciation into groups is based on the approach of Middleton et al. (1990). Biogenic emissions (isoprene, monoterpenes and nitrogen emissions by

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soil) have been calculated on-line from the Model on Emissions of Gases and Aerosols from Nature (MEGAN), using the USGS land-use classification and branch-level emission factors which incorporate canopy shading effects (Guenther et al., 2006). Two annual simulations have been performed with the integration of the emission inventory available in the WRF/Chem control (WCcntr) run and a modified emission inventory by applying country- and species-based multipliers as derived by the EE-IOA analysis in the WRF/Chem agriculture (WC-Agri20) run. PM2.5 concentrations and the impact of agriculture are assessed for each country and over all domain, and mortality estimates with the control and modified emissions are analyzed. The domain included in the study is shown in Fig. 2. 2.3. Excess mortality estimates The annual mean PM2.5 concentrations from the two simulations are used to estimate excess mortality rates for a range of related diseases and age groups, based on the integrated exposure-response (IER) functions as described in Burnett et al. (2014). For the calculation of the relative risk (RR) factors, which are a key part of the IERs, we used the updated parameters also used for the global burden of disease study (GBD) for 2015 (Cohen et al., 2017). In this study, IER functions are applied to account for health effects of PM2.5 related to ischaemic heart disease, cerebrovascular disease, lower respiratory tract infections, chronic obstructive pulmonary disease and lung cancer. We analyse the respective burden of disease for the age groups b5 years, 5–14, 15–29, 30–49, 50–69 and N70 years old. Country level baseline mortality rates for each of the diseases representative of the year 2010 and the population data for the countries included in our domain have been adopted from the WHO Global Health Observatory (http://www.who. int/gho/database/en/). 3. Results 3.1. IOA and EE-IOA multiplier analysis The IOA multiplier analysis identified the most important sectors of economic activity with regards to their capacity to generate economic output (Table 1). Industry creates the highest direct and indirect effects on the output of the EU-28 economy (2.19), that is, for a 1 million euro increase in the final demand for the products of the industrial sector, the total output of the European economy will be increased by 2.19 million euro. Significant backward linkages were created also by the construction (2.17) and the electricity, gas and water (2.12) sectors. On the

other hand, service sectors such as education (1.33), real estate (1.48) and public administration (1.50) create low multiplier effects. The EE-IOA analysis identified the contribution of sixteen economic sectors in the generation (direct and indirect) of air pollution emissions (Table 1). The water transport sector (shipping) generates large SOx (8.5) and NOx (23.7) emissions. The meaning of the water transport multipliers is that for every 1 million euro increase in the final demand for the products and services of the sector, 8.5 t of SOx and 23.7 t of NOx will be emitted. The industrial sector and the electricity, gas and water sectors are also important SOx and NOx emitters (Table 1). The water transport sector creates the highest direct and indirect PM2.5 (1.8) and PM10 (2) emissions across EU-28. Agriculture is the top NH3 emitter in the EU-28; for a 1 million euro increase in the final demand for the products of the sector, 11.4 t of NH3 will be emitted. The industrial sector is also an important NH3 emitter (Table 1). Agriculture is the most important NMVOC emitter (4.4) followed by the forestry sector (3.2) and the industrial sector (3.2). Agriculture (4.8) along with industry (5.4) and forestry (3.7) create the highest direct and indirect CO emissions. Health and Education were the sectors with the lowest air pollutant emissions. The 20% increase in final demand for the output of the agricultural sector across the EU-28 (Agri20 scenario), assuming no change in technology (aij), results in the following percentage changes in air pollution emissions compared to their initial 2010 levels: SOx emissions increase by 32.3%; NOx emissions increase by 25.4%; NH3 emissions increase by 23.2%; NMVOC emissions increase by 23.9%; CO emissions increase by 24.3%; PM2.5 emissions increase by 24.5%; PM10 emissions increase by 23.7%. Fig. 1 depicts the distribution of the agricultural air pollution emission multipliers for the EU-28 individual countries (Table A2). The highest SOx emission multipliers are reported in the agricultural sector of Poland (3.7) and Bulgaria (2), while the lowest appear in Spain (0.01) and Slovenia (0.01). Agriculture in Poland has also the highest NOx emission multiplier (10.5), followed by Czech-Republic (5.2) and Latvia (4.8). On the contrary, agriculture in Spain (0.4), Slovakia (1.1) and Malta (1.3) have the lowest NOx emission multipliers. The highest NH3 emission multipliers are observed in Luxemburg (17.9), Ireland (15.8), Estonia (15.7) and Poland (15), while the lowest are observed in Belgium (3.8), Netherlands (3.3) and Spain (1.3). In terms of NMVOC emissions, the highest multipliers appear in the UK (8.9) and Luxemburg (7), while the lowest ones appear in Spain (0.3), the Netherlands (0.1) and Malta (0.1). Poland has the largest CO emission multiplier (20.6), followed by Greece (6.5) and Portugal (5.4), while the Netherlands (0.4), Malta (0.3) and Slovakia (0.1) have the lowest ones. The highest PM2.5 emission multipliers are found in the agricultural sectors of Poland (1.6), Slovenia (0.8) and Estonia

Table 1 Economic output multipliers and air pollution emission multipliers computed with Eqs. (4) and (7) for EU-28 economic sectors (2010)a. Economic output (M€/M€)

Agriculture Forestry Industry Electricity, gas and water Construction Trade Land transport Water transport Air transport Accommodation and food services Banking – financing Real estate Public administration Education Health Other services

2.08 1.83 2.19 2.12 2.17 1.82 1.90 2.00 2.12 1.89 1.83 1.48 1.50 1.33 1.54 1.81

Air pollution emissions (tn/M€) SOx

NOx

NH3

NMVOC

CO

PM2.5

PM10

0.43 0.09 2.83 3.52 0.30 0.81 0.36 8.50 0.24 0.10 0.61 0.39 0.10 0.05 0.02 3.81

3.97 1.07 8.65 3.72 1.03 2.78 3.70 23.71 3.78 0.35 1.94 1.22 0.30 0.20 0.08 12.28

11.36 0.04 3.99 0.71 0.24 1.27 0.30 0.04 0.03 0.04 0.57 0.21 0.05 0.03 0.01 1.73

4.39 3.19 3.16 0.77 0.46 1.05 0.61 1.35 0.19 0.06 0.49 0.25 0.08 0.05 0.02 1.93

4.82 3.72 5.43 1.57 0.70 1.60 2.05 3.90 1.45 0.17 0.86 0.49 0.24 0.13 0.06 4.21

0.46 0.13 0.64 0.20 0.09 0.21 0.19 1.78 0.09 0.03 0.14 0.09 0.02 0.01 0.01 0.84

1.34 0.18 1.08 0.33 0.16 0.35 0.30 2.03 0.11 0.03 0.21 0.12 0.03 0.02 0.01 1.12

a The original EU-28 and the national IOA tables were derived from the Eurostat database (Eurostat, 2017) and the World Input-Output Database (Timmer et al., 2015). The air pollution emissions by sector of economic activity were derived from the Eurostat database (Eurostat, 2018a).

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Fig. 1. Agriculture's air pollutant (SOx, NOx, NH3, NMVOC, CO, PM2.5, PM10) emission multipliers for the EU-28 countries, classified into four quartiles (very low to very high).

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(0.4), while the lowest appear in the agricultural sectors of Slovakia (0.1) and Spain (0.06). The agricultural sector of Poland (3) is also the largest PM10 emitter in the EU-28, followed by Hungary (2.5), while Netherlands (0.2) and Spain (0.1) have the lowest ones. 3.2. Air quality impact Two simulations are performed in the context of the present work: one with current (CNTR) and one with the perturbed (Agri20) emissions based on the multiplier factors derived from the EE-IOA analysis (the percentage increase of the species-country emissions due to the agricultural growth is summarised in Table A3). We focus on changes in PM2.5 as the major contributor to outdoor pollution leading to excess mortality. The additional PM2.5 burden over Europe due to the increase in emissions of aerosol precursors is shown in Fig. 2, expressed as CPM2.5Agri20 – CPM2.5cntr where CPM2.5Agri20 are the surface concentrations of PM2.5 from the modified (Agri20) simulation and CPM2.5cntr those from the control (CNTR) simulation. The distribution of the differences in near-ground concentrations (related to exposure and health impacts) shows a spatial peak of the mean annual average PM2.5 over East Europe and specifically over the northern Balkan countries reaching 9–10 μg m−3 over Bulgaria and Romania. Both countries exhibit a large response to the enhancement of the agricultural activity in terms of increased emissions, especially for SOx (461% and 2762%, respectively) (Table A3). The agricultural sector in Bulgaria and Romania, although it has low direct SOx emissions per unit of economic output (0.09 tn/M€ and 0.004 tn/M€, respectively), it generates high total (direct and indirect) SOx emissions per unit of economic output (2.04 tn/M€ and 0.60 tn/M€, respectively) due to the strong backward linkages with the industry and the electricity sector (Table A2). This interdependence of agriculture with the two most SOx emission-intensive sectors can explain the large increase of SOx emissions under the Agri20 scenario. For example, in Bulgaria the electricity sector emits 75.3 t of SOx per million euro of economic output (18.7 tn/M€ for Romania). Another region of pronounced additional PM2.5 (6–8 μg m−3) due to the modification of emissions is Italy and especially northern Italy. Sulphur oxide emissions are increased by orders of magnitude in Italy due to agricultural growth. However, an additional contribution is from primary PM2.5 emissions that respond strongly to the agricultural enhancement (32.4%). The increase in emission fluxes in this region affects PM2.5 over the Central Mediterranean and the island of Malta making it prone to both local and transported PM2.5 pollution. The impact of the increase in agricultural and related emissions is evident far downwind over Northern Africa with an increase in aerosol burden that reaches 5–6.5 μg m−3. 3.3. Health assessment Subsequently, we quantify the health burden of each EU-28 country (details in Table A4) deriving from the Agri20 scenario (Fig. 3). The least affected countries, with enhanced mortality of b20% compared to the control run, are mostly located in the northern part of Europe. These countries (Supplementary material Table A4, low sensitivity column) have comparable NOx aerosol precursor multipliers (20–26%) but their SOx multipliers range from 22 to 50%. Despite the wide range of SOx increases these countries are less affected due to limited pollution transport and the contribution of SOx to aerosol formation mainly far from source areas (Park et al., 2014). Exceptions are Finland and Sweden, which exhibit a 44.6 and 38.2% increase in premature deaths, despite low PM2.5 excess burden. Malta, Cyprus, Greece and Spain are the countries affected mostly by the changes in emissions and enhanced PM2.5 concentrations, mainly due to their location downwind of major pollution sources and emissions from the agricultural sector. The increase in excess mortality in these countries ranges from 49.5% (Cyprus) to 89.1% (Malta).

Two of these countries, with an increase in excess mortality exceeding 50%, are Greece and Spain. Despite being less affected in terms of surface PM2.5 concentrations (2–4 μg m−3 increase in the Agri20 compared to CNTR simulation) we find a 50.3% increase in excess deaths in Spain, a value comparable to the 56.5% increase in Greece where PM2.5 levels were enhanced by approximately 5–7 μg m−3. The mean increase in excess mortality rates due to the enhancement of the agricultural sector in EU-28 countries, as well as the entire domain (all countries included in the model calculations) is 24%. 4. Discussion The assessment of the direct and indirect contribution of the production sectors of the EU-28 economy to air pollution indicates that the water transport sector (shipping) generates the largest SOx, NOx, PM2.5 and PM10 emissions per million euro increase in the final demand for the products and services of the sector across the region. Ship emissions have significant impact on air quality, not only along the transport routes but also over a large part of the European continent (Aksoyoglu et al., 2016). Viana et al. (2014) estimated that on average shipping emissions in Europe contribute by 1–7% to the ambient air PM10 levels, by 1–14% to PM2.5, and at least 11% to PM1, with the maximum contribution observed over the Mediterranean Sea and the North Sea. More precisely, Aksoyoglu et al. (2016) found that ship emissions increase the particulate sulphate concentrations over the Mediterranean Sea (up to 60%) and the North Sea (30–35%), while increases in particulate nitrate concentrations were found in the northern part of Europe, around the Benelux area (20%), where land-based NH3 emissions are high. The relatively large contribution of ship emissions to primary gaseous pollutants concentrations in the coastal areas of the North Sea were also reported by Aulinger et al. (2016). Aksoyoglu et al. (2016) conclude that the combination of NOx emissions from ships and NH3 emissions from agriculture will play a significant role in future European air quality. The EE-IOA multiplier analysis for the agricultural sector of the individual EU-28 countries revealed that the total (direct and indirect) air pollution emission multipliers follow the pattern of the direct air pollutant emission multipliers in most European countries. This pattern can be attributed to the weak backward linkages of agriculture with other economic sectors (Giannakis et al., 2014). For example, countries such as Poland with high direct air pollution emissions per unit of agricultural economic output (e.g., SOx: 2.83 tn/€; NOx: 8.29 tn/€, CO: 16.34 tn/€; PM2.5: 1.31 tn/€) also exhibit high direct and indirect air pollutant emissions (SOx: 3.71 tn/€; NOx: 10.49 tn/€; CO: 20.60 tn/€; PM2.5: 1.64 tn/€). Specifically for Poland, agriculture creates relatively low backward linkages with the rest economic sectors (11th in rank out of 16 sectors). Thus, an increase in agricultural production does not significantly increase air pollution emissions of other sectors. Gurgul and Lach (2015) reported that the importance of agriculture in Poland in terms of its ability to generate multiplier effects throughout the economy was reduced during its EU accession in 2004. However, there are cases in our analysis that highlight the importance of the indirect effects of economic activity in the generation of air pollution. This applies in particular to SOx emissions, because for half of the EU-28 countries the indirect effects of agricultural economic activity are higher than the direct effects, mainly due to linkages with industry. For example, the agricultural sector in Italy has the second lowest direct SOx emission multiplier per million euro of economic activity (0.0006 tn/M€) but the third highest total (direct and indirect) SOx emission multiplier (0.84 tn/M€). This high multiplier effect can be attributed to the strong linkages of agriculture with industry; around 45% of the intermediate inputs for the production of agricultural products in Italy sources from industry, that is, the sector with the second highest contribution in the generation of SOx emissions (136,778 tn). Thus, an increase of agricultural output generates significant (direct and indirect) SOx emissions. Similarly, the agricultural sector in the UK has the highest NMVOC emission

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Fig. 2. Modelled PM2.5 surface concentrations from the CNTR simulation (top plot) and PM2.5 excess pollution from the Agri20 simulation.

multiplier across EU-28 mainly due to the high backward linkages it creates with industry, which generates around 50% of the total NMVOC emissions. The perturbed emission fluxes from this experiment are anticipated to modify the fine particulate matter landscape over our domain. SO2 and NOx are oxidised in the atmosphere to form secondary sulphate and nitrate particles, respectively. Ammonium nitrate (NH4NO3) aerosols are the product of reaction between ammonia (NH3) and nitric acid (HNO3) that in turn results from the photochemical oxidation of NOx. The process of production of NH4NO3 competes with the slowerrate formation of ammonium sulphate (NH₄)₂SO₄, in which gaseous ammonia neutralizes the sulfuric acid aerosols in the atmosphere. During transport away from the source areas of its precursors (NOx and NH3), and especially with warmer temperature, NH4NO3 can rapidly volatilize away in contrast with the more thermodynamically stable (NH₄)₂SO₄. Therefore NH4NO3 is predicted to contribute to aerosol optical depth (AOD) mostly over polluted regions (Park et al., 2014). Thus, the nitrate component of PM2.5 dominates near emission sources while sulphate formation is slower, hence it contributes to PM2.5 further downwind. The response of the PM2.5 surface concentrations to the increase in emissions is a combination of the chemistry (aerosol formation) and meteorology (aerosol transport and removal). Simulations with the chemistry-transport model WRF/Chem, using the control and perturbed emission inventories, revealed that the highest impact of the growth of the agricultural sector on PM2.5 levels across EU-28 occurs in northern Balkan countries as a response to the significantly increased aerosol

precursor levels, mainly SO2 and sulphate downwind of major production areas. Analysing PM2.5 sub-components of fine particulate matter distribution over Europe, Kushta et al. (2018) showed that the main contributor to the total anthropogenic PM2.5 load in the Eastern and Southern Europe is sulphate, and to a lesser extent organic carbon, associating this region with a more acidic environment than Central and Western Europe; over the latter area nitrate ammonium aerosols are more abundant. Thus, the pronounced increase in SOx emissions over Bulgaria and Romania (more than two and three orders of magnitude, respectively) results in an intensification of the aerosol formation processes over these countries. The comparison of premature mortality estimates based on the PM2.5 levels, as simulated with the current and perturbed emissions, showed that several northern countries are among the least affected (b15% increase in excess mortality rates). These countries are less influenced by the enhancement of the agricultural sector due to their location in view of atmospheric circulation patterns and pollution transport, despite similar increases in the emission fluxes of the main aerosol precursors. The respective increases in SO2 vary from 22 to 50%, however due to the nature of the sulphate aerosol formation processes (remote from source regions) the impact of this increase is more evident downwind. It must be noted that changes in estimated excess mortality rates do not follow a linear relationship with PM2.5 concentrations. The relative contribution of any additional source of pollution depends on the baseline pollution level and is affected by the collocation of population

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Fig. 3. Country-level excess mortality rate estimates in the EU-28 due to outdoor air pollution levels of PM2.5 from the Agri20 scenario relative to the control PM2.5 concentrations, classified into three categories: low sensitivity (mortality increase b20%), medium sensitivity (mortality increase between 20 and 30%) and high sensitivity (mortality increase N30%).

density with the pollution change. One such example is the increase in excess mortality (50.3%) from aerosol precursor emissions in Spain. This can be partially explained by the fact that most of the excess PM2.5 burden over Spain occurs in the eastern coastal areas (Cataluña, Valenciana, Illes Balears, Andalucía, Murcia, Ceuta, Melilla), which have a higher population density (153 persons km−2) than the rest of the country (44 persons km−2; excluding Madrid) (Eurostat, 2018c, 2018d). Another reason for the similar impact on mortality, despite different responses to increasing emissions, is that IERs are non-linear, with the consequence that areas with relatively low reference pollution (Spain) are influenced more strongly in terms of increasing excess mortality from PM2.5 compared to more base-line polluted areas (such as Greece). Another example related to the non-linearity of IERs is the large increase in excess mortality over Scandinavian countries (Finland and Sweden) in response to a relatively small enhancement of air pollution. Our findings highlight the significant contribution of agriculture to air pollution emissions. Bauer et al. (2016) estimated that PM2.5 emissions from livestock production and the use of nitrogen fertilizers are responsible for 55% of air pollution linked to human activities in Europe. The reduction of NH3 emissions has been considered as the most effective control strategy for mitigating PM2.5 over Europe (Megaritis et al., 2013; Bessagnet et al., 2014). Megaritis et al. (2013) investigated changes in PM2.5 concentrations in Europe in response to a 50% reduction of NH3 emissions. Their model results indicated a decrease of PM2.5 up to 5.1 μg m−3 and 1.8 μg m−3 during summer and winter, that is, 5.5 and 4% on average, respectively. Similarly, Pozzer et al. (2017) applied a global chemistry-climate model (EMAC) to study the impacts of NH3 emissions on PM2.5. Model results indicated that a reduction of agricultural emissions by 50% could reduce the annual, geographical average near-surface PM2.5 concentrations up to 11% in Europe and reduce the mortality attributable to air pollution by 19% over Europe. The European Commission's (2013) Clean Air Programme for Europe (COM(2013)918 final) has set out a roadmap for reducing air pollution in Europe. The Directive 2016/2284/EU on the reduction of

national emissions of certain atmospheric pollutants has established an obligation for all EU countries to reduce, among other pollutants, the NH3 emissions by 6% for the period from 2020 to 2029, relative to 2005, while from 2030 onwards the national reduction commitment is set at 19%. The Directive also allocates high priority on the controls of primary PM2.5 emissions. The implementation of technology and fostering research and development towards efficiency improvements in agriculture can help achieve reductions of pollution emissions. A wide range of technological advancements and options are already available to regulate agricultural emissions, but have yet to be adopted at the scale and intensity that is necessary to deliver substantial reductions (EEA, 2017). Giannadaki et al. (2018) analyzed the costs and benefits of five selected NH3 emission abatement measures, namely, low nitrogen feed, low emission animal housing, low and high efficiency manure storage capacity, and improved application of urea fertilizer. Their estimates revealed that the reduction of agricultural emissions generates a large net economic benefit for the EU under all abatement options, the largest of which results from the adoption of the highly efficient techniques for manure storage, that is, 163 billion US$, thus providing strong support for the enforcement of the measures. Considering that the implementation of such measures may negatively influence the competitiveness of the European farm holdings, new financial incentives under the Pillar 2 farm modernization investment schemes, or the development of an enhanced cross-compliance scheme that could further integrate agricultural policies with the environmental and climate policies, could counterbalance the implementation costs of NH3 emission abatement measures and promote their adoption. For example, Oenema et al. (2009) suggest that ammonia emission abatement measures should be implemented together with integrated nitrogen management measures to achieve the targets of both the National Emission Ceilings Directive (2001/81/EC) and the Nitrate Directive (91/676/EEC). Further, Wagner et al. (2015) support that all relevant emission types should be integrated in the design of air pollution abatement and climate change mitigation policies to stimulate

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synergies and to define the optimum level of emission reduction targets due to interactions between NH3, PM and GHG emission abatement measures. Investing in farm training schemes and advisory services could foster the uptake of new technologies and improve the economic and environmental performance of agriculture (Giannakis and Bruggeman, 2018). Finally, shifts in dietary patterns from high air pollutant emission foods (e.g., meat and dairy) to low air pollutant emission foods (e.g., plant-based food products) can further contribute to the reduction of ammonia emissions (Sheppard and Bittman, 2015). Measures that foster these changes in diets, e.g., air pollutant emission taxes on the consumption side of animal-based foods, can offer great potential for reducing agricultural emissions (Springmann et al., 2017).

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impacts to the emission fluxes. This is defined by the chemical nature of aerosol formation processes and the circulation patterns over the European continent, as well as the non-linear behavior of integrated exposure-response functions, especially at relatively low PM2.5 concentrations. In terms of excess mortality the most affected countries are those downwind of emission sources and with collocated high pollution and population densities. Time series analyses incorporating future structural change could further scrutinize the role of agriculture in improving air quality and health conditions. Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.01.410. Conflict of interest

5. Conclusions The authors state that they have no conflicts of interest to disclose. By combining EE-IOA models with a regional atmospheric chemistry-transport model, we explored the economy-wide effects of agriculture on air quality and human health across EU-28 countries. We assessed the impact of a 20% increase in the final demand for the output of agriculture on PM2.5 levels and excess mortality estimates. The results of the integrated modelling framework revealed significant differences in the air pollution intensities of the agricultural sector across Europe, highlighting the importance of capturing both the direct and indirect air pollutant emissions of economic sectors via upstream supply chains. The simulations of the regional atmospheric model underscore the non-linear response of surface PM2.5 levels as well as their health

Acknowledgements The Computational resources and support were provided by the European Union Horizon 2020 research and innovation programme VI-SEEM project (grant agreement 675121). We acknowledge the use of the anthropogenic emission datasets downloaded from http://edgar.jrc.ec.europa.eu/htap_v2/. The emissions were prepared in the framework of the Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) organized in 2005 under the auspices of the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP Convention).

Appendix A Table A1 NACE (Statistical classification of economic activities in the European Union) codes of the sectors of economic activity that make up the 16 sectors for the input-output analysis for EU-28 countries (2010). Source: Eurostat (2008). n/n

Sector

Description NACE

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Agriculture Forestry Industry Electricity, gas & water Construction Trade Land transport Water transport Air transport Accommodation and food services Banking - financing Real estate Public administration Education Health Other services

CPA A01, A03 CPA A02 CPA B, C CPA D, E CPA F CPA G CPA H49 CPA H50 CPA H51 CPA I CPA K CPA L CPA O94 CPA P85 CPA Q86-Q88 CPA J58-63, M69-75, N, R, S, T, U

Table A2 Agriculture's air pollutant (SOx, NOx, NH3, NMVOC, CO, PM2.5, PM10) emission multipliers for the EU-28 countries, as computed with the environmentally-extended input-output analysis. EU-28 countries

SOx

Austria Belgium Bulgaria Croatia Cyprus Czech-Republic Denmark Estonia Finland France Germany

0.03 0.15 2.04 0.54 0.47 0.11 0.28 0.78 0.34 0.14 0.07

NOx 2.63 1.35 3.16 3.29 1.48 5.19 3.99 3.56 2.24 3.33 2.64

NH3 9.63 3.79 7.17 8.70 5.81 8.88 7.54 15.66 6.89 10.62 10.18

NMVOC 1.75 2.34 7.02 2.98 1.94 0.52 4.10 6.95 0.74 5.45 3.45

CO 3.96 0.70 1.44 1.11 0.41 2.73 2.19 1.03 4.05 2.61 1.30

PM2.5

PM10

0.41 0.12 0.13 0.26 0.17 0.40 0.36 0.44 0.23 0.40 0.24

1.03 0.44 0.34 1.03 0.66 1.46 1.34 1.90 0.62 1.38 0.83

(continued on next page)

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Table A2 (continued) EU-28 countries

SOx

NOx

NH3

Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK

0.31 0.03 0.08 0.84 0.15 0.10 0.00 0.02 0.13 3.71 0.31 0.60 0.05 0.01 0.01 0.12 0.13

2.72 4.22 1.51 2.71 4.8 3.02 3.82 1.3 2.19 10.49 4.73 1.48 1.1 3.05 0.4 3.17 1.44

4.16 12.19 15.80 6.84 11.77 14.42 17.92 7.39 3.34 14.97 8.26 12.70 12.31 12.83 1.32 6.23 6.98

NMVOC 0.53 3.83 6.28 0.36 5.43 4.24 7.05 0.12 0.13 2.98 0.95 6.60 7.04 4.93 0.25 4.71 8.89

CO 6.54 0.92 0.39 2.16 1.35 0.84 0.40 0.35 0.38 20.60 5.45 1.45 0.11 1.99 0.61 3.76 3.78

PM2.5

PM10

0.43 0.38 0.36 0.38 0.37 0.22 0.21 0.17 0.13 1.64 0.43 0.38 0.11 0.76 0.06 0.26 0.43

0.87 2.49 1.70 0.62 2.33 0.68 1.00 0.58 0.21 3.03 0.58 2.18 1.11 1.48 0.15 0.78 0.83

Table A3 Species-country emission increase due to the Agri20 scenario (20% increase in demand for agricultural output) for SOx, NOx, NH3, NMVOC, CO, PM2.5 and PM10, in % relative to the control simulation. EU-28 countries

SOx

NOx

NH3

NMVOC

Austria Belgium Bulgaria Croatia Cyprus Czech-Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxemburg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK

27.0 23.0 460.6 38.6 175.1 46.0 38.6 165.6 29.6 31.0 36.6 190.2 66.4 29.9 3003.0 27.7 49.8 22.1 120.6 74.4 26.2 40.2 2761.9 119.6 451.8 45.0 59.2 45.7

24.1 22.5 31.8 30.1 31.4 21.8 26.0 28.4 25.8 24.7 22.2 25.7 27.2 25.0 37.7 25.1 24.2 20.1 22.3 28.2 25.3 23.9 55.5 29.6 23.3 23.5 24.3 26.0

23.8 21.2 26.0 23.8 21.8 21.5 22.2 23.9 23.6 23.6 20.9 23.0 25.6 23.3 22.2 24.0 22.1 20.1 21.7 22.7 24.8 22.2 28.3 23.6 22.9 21.1 22.1 22.0

23.9 21.6 26.4 25.1 22.4 23.1 22.5 24.1 24.3 23.8 21.7 25.9 25.9 23.3 27.0 24.3 22.9 20.1 24.0 28.1 25.8 24.3 28.9 23.7 22.9 22.4 22.2 60.1

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