scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub> and PM<sub>2.5</sub> for 2001–2010

2016 ◽  
Author(s):  
C. Lin ◽  
M. R. Heal ◽  
M. Vieno ◽  
I. A. MacKenzie ◽  
B. G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Transport Processes ◽  
Urban Traffic ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Pm2.5 And Pm10 ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km × 5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model-measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2.5 and PM10 (daily maximum 8-hour running mean for O3). The comparison was temporally and spatially comprehensive covering a 10-year period (2 years for PM2.5) and all measurement data from the UK national reference monitor network, which applies consistent operational and QC/QA procedures for each pollutant (60, 49, 29 and 35 sites for NO2, O3, PM2.5 and PM10, respectively). The two most important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – were evaluated by site type, year, month and day-of-week. Model-measurement correlation and bias were generally better than values found in past studies that allowed for measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2 and PM2.5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2.5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at the background sites and least bias at rural background sites (median normalised mean bias (NMB) values for daily O3 and NO2 of 8 % and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB = −29 %) and PM2.5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km × 5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions than the grid average. This effect was particularly pronounced for comparison against urban traffic monitors, which are deliberately located close to strong sources of NOx and PM. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. wind-blown dust. There were instances of monthly and weekday/weekend variations in extent of model-measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model-measurement differences (aside from grid vs monitor spatial representivity) was inaccuracy of model emissions (both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals) rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

scholarly journals Spatiotemporal evaluation of EMEP4UK-WRF v4.3 atmospheric chemistry transport simulations of health-related metrics for NO<sub>2</sub>, O<sub>3</sub>, PM<sub>10</sub>, and PM<sub>2. 5</sub> for 2001–2010

2017 ◽  
Vol 10 (4) ◽  
pp. 1767-1787 ◽  
Author(s):  
Chun Lin ◽  
Mathew R. Heal ◽  
Massimo Vieno ◽  
Ian A. MacKenzie ◽  
Ben G. Armstrong ◽  
...  
Keyword(s):  
Air Pollution ◽  
Atmospheric Chemistry ◽  
Measurement Data ◽  
Transport Processes ◽  
Daily Maximum ◽  
Model Bias ◽  
Model Framework ◽  
Urban Background ◽  
Air Pollution Epidemiology ◽  
Model Measurement

Abstract. This study was motivated by the use in air pollution epidemiology and health burden assessment of data simulated at 5 km  ×  5 km horizontal resolution by the EMEP4UK-WRF v4.3 atmospheric chemistry transport model. Thus the focus of the model–measurement comparison statistics presented here was on the health-relevant metrics of annual and daily means of NO2, O3, PM2. 5, and PM10 (daily maximum 8 h running mean for O3). The comparison was temporally and spatially comprehensive, covering a 10-year period (2 years for PM2. 5) and all non-roadside measurement data from the UK national reference monitor network, which applies consistent operational and QA/QC procedures for each pollutant (44, 47, 24, and 30 sites for NO2, O3, PM2. 5, and PM10, respectively). Two important statistics highlighted in the literature for evaluation of air quality model output against policy (and hence health)-relevant standards – correlation and bias – together with root mean square error, were evaluated by site type, year, month, and day-of-week. Model–measurement statistics were generally better than, or comparable to, values that allow for realistic magnitudes of measurement uncertainties. Temporal correlations of daily concentrations were good for O3, NO2, and PM2. 5 at both rural and urban background sites (median values of r across sites in the range 0.70–0.76 for O3 and NO2, and 0.65–0.69 for PM2. 5), but poorer for PM10 (0.47–0.50). Bias differed between environments, with generally less bias at rural background sites (median normalized mean bias (NMB) values for daily O3 and NO2 of 8 and 11 %, respectively). At urban background sites there was a negative model bias for NO2 (median NMB  =  −29 %) and PM2. 5 (−26 %) and a positive model bias for O3 (26 %). The directions of these biases are consistent with expectations of the effects of averaging primary emissions across the 5 km  ×  5 km model grid in urban areas, compared with monitor locations that are more influenced by these emissions (e.g. closer to traffic sources) than the grid average. The biases are also indicative of potential underestimations of primary NOx and PM emissions in the model, and, for PM, with known omissions in the model of some PM components, e.g. some components of wind-blown dust. There were instances of monthly and weekday/weekend variations in the extent of model–measurement bias. Overall, the greater uniformity in temporal correlation than in bias is strongly indicative that the main driver of model–measurement differences (aside from grid versus monitor spatial representivity) was inaccuracy of model emissions – both in annual totals and in the monthly and day-of-week temporal factors applied in the model to the totals – rather than simulation of atmospheric chemistry and transport processes. Since, in general for epidemiology, capturing correlation is more important than bias, the detailed analyses presented here support the use of data from this model framework in air pollution epidemiology.

scholarly journals Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements

2021 ◽  
Vol 14 (11) ◽  
pp. 7021-7046
Author(s):  
Yao Ge ◽  
Mathew R. Heal ◽  
David S. Stevenson ◽  
Peter Wind ◽  
Massimo Vieno
Keyword(s):  
North America ◽  
Southeast Asia ◽  
East Asia ◽  
Atmospheric Chemistry ◽  
Wet Deposition ◽  
Global Analysis ◽  
Monitoring Networks ◽  
Model Framework ◽  
Annual Total ◽  
Model Measurement

Abstract. Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (Nr) species, especially of reduced N (gaseous NH3, particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of Nr chemistry and its potential mitigation. Here we undertake the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1∘×1∘ resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The model–measurement comparison is conducted both spatially and temporally, covering 10 monitoring networks worldwide. Model simulations for 2010 compared use of both HTAP and ECLIPSEE (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSEE only. Simulations of primary pollutants are somewhat sensitive to the choice of inventory in places where regional differences in primary emissions between the two inventories are apparent (e.g. China) but are much less sensitive for secondary components. For example, the difference in modelled global annual mean surface NH3 concentration using the two 2010 inventories is 18 % (HTAP: 0.26 µg m−3; ECLIPSEE: 0.31 µg m−3) but is only 3.5 % for NH4+ (HTAP: 0.316 µg m−3; ECLIPSEE: 0.305 µg m−3). Comparisons of 2010 and 2015 surface concentrations between the model and measurements demonstrate that the model captures the overall spatial and seasonal variations well for the major inorganic pollutants NH3, NO2, SO2, HNO3, NH4+, NO3-, and SO42- and their wet deposition in East Asia, Southeast Asia, Europe, and North America. The model shows better correlations with annual average measurements for networks in Southeast Asia (mean R for seven species: R7‾=0.73), Europe (R7‾=0.67), and North America (R7‾=0.63) than in East Asia (R5‾=0.35) (data for 2015), which suggests potential issues with the measurements in the latter network. Temporally, both model and measurements agree on higher NH3 concentrations in spring and summer and lower concentrations in winter. The model slightly underestimates annual total precipitation measurements (by 13 %–45 %) but agrees well with the spatial variations in precipitation in all four world regions (0.65–0.94 R range). High correlations between measured and modelled NH4+ precipitation concentrations are also observed in all regions except East Asia. For annual total wet deposition of reduced N, the greatest consistency is in North America (0.75–0.82 R range), followed by Southeast Asia (R=0.68) and Europe (R=0.61). Model–measurement bias varies between species in different networks; for example, bias for NH4+ and NO3- is largest in Europe and North America and smallest in East Asia and Southeast Asia. The greater uniformity in spatial correlations than in biases suggests that the major driver of model–measurement discrepancies (aside from differing spatial representativeness and uncertainties and biases in measurements) are shortcomings in absolute emissions rather than in modelling the atmospheric processes. The comprehensive evaluations presented in this study support the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA.

scholarly journals Evaluation of global EMEP MSC-W (rv4.34)-WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements

2021 ◽  
Author(s):  
Yao Ge ◽  
Mathew R. Heal ◽  
David S. Stevenson ◽  
Peter Wind ◽  
Massimo Vieno
Keyword(s):  
North America ◽  
Southeast Asia ◽  
East Asia ◽  
Atmospheric Chemistry ◽  
Wet Deposition ◽  
Global Analysis ◽  
Monitoring Networks ◽  
Model Framework ◽  
Annual Total ◽  
Model Measurement

Abstract. Atmospheric pollution has many profound effects on human health, ecosystems, and the climate. Of concern are high concentrations and deposition of reactive nitrogen (Nr) species, especially of reduced N (gaseous NH3, particulate NH4+). Atmospheric chemistry and transport models (ACTMs) are crucial to understanding sources and impacts of Nr chemistry and its potential mitigation. Here we undertake the first evaluation of the global version of the EMEP MSC-W ACTM driven by WRF meteorology (1° × 1° resolution), with a focus on surface concentrations and wet deposition of N and S species relevant to investigation of atmospheric Nr and secondary inorganic aerosol (SIA). The model-measurement comparison is conducted both spatially and temporally, covering 9 monitoring networks worldwide. Model simulations for 2010 compared use of both HTAP and ECLIPSEE (ECLIPSE annual total with EDGAR monthly profile) emissions inventories; those for 2015 used ECLIPSEE only. Simulations of primary pollutants are somewhat sensitive to the choice of inventory in places where regional differences in primary emissions between the two inventories are apparent (e.g. China), but much less so for secondary components. For example, the difference in modelled global annual mean surface NH3 concentration using the two 2010 inventories is 18 % (HTAP: 0.26 μg m−3; ECLIPSEE: 0.31 μg m−3) but only 3.5 % for NH4+ (HTAP: 0.316 μg m−3; ECLIPSEE: 0.305 μg m−3). Comparisons of 2010 and 2015 surface concentrations between model and measurement demonstrate that the model captures well the overall spatial and seasonal variations of the major inorganic pollutants NH3, NO2, SO2, HNO3, NH4+, NO3−, SO42−, and their wet deposition in East Asia, Southeast Asia, Europe and North America. The model shows better correlations with annual average measurements for networks in Southeast Asia (Mean R for 7 species:  = 0.73), Europe ( = 0.67) and North America ( = 0.63) than in East Asia ( = 0.35) (data for 2015), which suggests potential issues with the measurements in the latter network. Temporally, both model and measurement agree on higher NH3 concentrations in spring and summer, and lower concentrations in winter. The model slightly underestimates annual total precipitation measurements (by 13–34 %) but agrees well with the spatial variations in precipitation in all four world regions (0.65–0.78 R range). High correlations between measured and modelled NH4+ precipitation concentrations are also observed in all regions except East Asia. For annual total wet deposition of reduced N, the greatest consistency is in North America (R = 0.75), followed by Southeast Asia (R = 0.68) and Europe (R = 0.61). Model-measurement bias varies between species in different networks; for example, bias for NH4+ and NO3− is most in Europe and North America and least in East and Southeast Asia. The greater uniformity in spatial correlations than in biases suggests that the major driver of model-measurement discrepancies (aside from differing spatial representativeness and uncertainties and biases in measurements) are shortcomings in absolute emissions rather than in modelling the atmospheric processes. The comprehensive evaluations presented in this study support the application of this model framework for global analysis of current and potential future budgets and deposition of Nr and SIA.

scholarly journals Nitrogen dioxide reductions from satellite and surface observations during COVID-19 mitigation in Rome (Italy)

2021 ◽  
Author(s):  
Cristiana Bassani ◽  
Francesca Vichi ◽  
Giulio Esposito ◽  
Mauro Montagnoli ◽  
Marco Giusto ◽  
...  
Keyword(s):  
Air Pollution ◽  
Nitrogen Dioxide ◽  
Rural Areas ◽  
Urban Traffic ◽  
Vertical Column ◽  
Urban Background ◽  
Entire Area ◽  
Rural Background ◽  
Surface Measurements ◽  
Rural Sites

AbstractLockdown restrictions were implemented in Italy from 10 March 2020 to contain the COVID-19 pandemic. Our study aims to evaluate air pollution changes, with focus on nitrogen dioxide (NO2), before and during the lockdown in Rome and in the surroundings. Significant NO2 declines were observed during the COVID-19 pandemic with reductions of − 50%, − 34%, and − 20% at urban traffic, urban background, and rural background stations, respectively. Tropospheric NO2 vertical column density (VCD) from the TROPOspheric Monitoring Instrument (TROPOMI) was used to evaluate the spatial-temporal variations of the NO2 before and during the lockdown for the entire area where the surface stations are located. The evaluation is concerned with the pixels including one or more air quality stations to explore the capability of the unprecedented high spatial resolution to monitor urban and rural sites from space with relation to the surface measurements. Good agreement between surface concentration and TROPOMI VCD was obtained in Rome (R = 0.64 in 2019, R = 0.77 in 2020) and in rural sites (R = 0.71 in 2019). Inversely, a slight correlation (R = 0.20) was observed in rural areas during the lockdown due to very low levels of NO2. Finally, the TROPOMI VCD showed a sharp decline in NO2, larger in urban (− 43%) than in rural sites (− 17%) as retrieved with the concurrent surface measurements averaging all the traffic and urban background (− 44%) and all the rural background stations (− 20%). These results suggest air pollution improvement in Rome gained from implementing lockdown restrictions.

scholarly journals Neighbourhood-scale flow regimes and pollution transport in cities

2020 ◽  
Author(s):  
Ed Bannister ◽  
Xiaoming Cai ◽  
Jian Zhong ◽  
Rob MacKenzie
Keyword(s):  
Air Pollution ◽  
Porous Media ◽  
Urban Form ◽  
Flow Regimes ◽  
Transport Processes ◽  
Pollution Monitoring ◽  
Pollution Transport ◽  
Urban Background ◽  
Neighbourhood Scale ◽  
Quantitative Definition

&lt;p&gt;Cities intimately intermingle people and air pollution. However, is very difficult to assess the efficacy of air pollution policy. Permanent in-situ observations are usually too sparsely spaced to monitor transport processes within a city. The post-processing and maintenance costs associated with calibrated low-cost sensors remains too high for them simply to fill the gaps in permanent networks. The behaviour of pollutants around the scale of a neighbourhood (1-2km) remains particularly difficult to interpret and model. This gap in our understanding is unfortunate because neighbourhood-scale processes disperse pollutants from peaks beside busy roads to levels treated as the &amp;#8216;urban background&amp;#8217;, and may link urban pollution models with weather forecasts.&lt;/p&gt;&lt;p&gt;Urban areas can be treated as patches of porous media to which the wind adjusts by changing its mean and turbulent components. Most cities around the world are made up of lots of neighbourhoods of differing form, density and land use &amp;#8211; e.g. commercial centres interspaced with low-rise residential neighbourhoods. For cities whose urban form varies in this way, we formulated two neighbourhood-scale flow regimes, based on the size and density of the different neighbourhood patches.&lt;/p&gt;&lt;p&gt;We used large-eddy simulation to investigate how these two dynamical regimes emerge in patchy neighbourhoods, and their implications for pollution policy and research. We found that these flow regimes distribute pollutants in counter-intuitive ways, such as producing pollution &amp;#8216;hot spots&amp;#8217; in less dense patches. The flow regimes also provide: (a) a quantitative definition of the &amp;#8216;urban background&amp;#8217;, which can be used for more precisely targeted pollution monitoring; and (b) a conceptual basis for neighbourhood-scale air pollution problems and transport of fluid constituents in other porous media.&lt;/p&gt;

Effect of transport measures and curfew against COVID-19 spread on air pollution (TSP) in Ecuador’s three most populous cities

2020 ◽  
Keyword(s):  
Air Pollution ◽  
Sustainable Consumption ◽  
State Level ◽  
Emergency Situation ◽  
Traffic Emission ◽  
Policy Makers ◽  
Total Suspended Particulates ◽  
Level Ii ◽  
Pm2.5 And Pm10 ◽  
Future Consequences

Although current circumstances pose challenges to foretelling the future consequences of coronavirus spread, we consider environmental load-related researches became more and more important nowadays perhaps as never before. Many experts believe that the increasingly dire public health emergency situation, policy makers and word leaders should make it possible that the COVID-19 outbreak contributes to a transition of sustainable consumption. With the purpose of contributing to rethink the importance of sustainability efforts, here we present total suspended particulates (TSP) results which represent traffic emission caused air pollution in the three most populous cities of Ecuador obtained before, during, and after the: (i) the traffic measures entered into force on state level; (ii) curfew entered into force on state level; (iii) and quarantine entered into force (in Guayaquil, and whole Guayas province). We documented significant decrease in TSP emissions (PM2.5 and PM10) compared to normal traffic operation obtained from some four lanes roads in Quito, Guayaquil, and Cuenca. The most remarkable fall in suspended particulate values (96.47% decrease in PM2.5) compared to emission observed before traffic measures occurred in Cuenca.

Comprehensive Pricing Model of Urban Traffic Air Pollution Based on Bi-Level Programming

2012 ◽  
Vol 253-255 ◽  
pp. 1913-1917
Author(s):  
Ze Bin Zhao
Keyword(s):  
Air Pollution ◽  
Negative Impact ◽  
Urban Traffic ◽  
Vehicle Speed ◽  
Pricing Model ◽  
The Sustainable Development ◽  
Transport Services ◽  
Relationship Model ◽  
The Relationship

In order to reduce the negative impact of urban traffic air pollution, this paper firstly analyzes the relationship between urban traffic air pollution and vehicle speed, after providing the relationship model, the paper establishes a comprehensive pricing model of urban traffic air pollution based on bi-level programming, the model considers the traffic air pollution pricing, and includes the factors of congestion pricing, bus fee, pricing revenue redistribution on improvement of public transport services and the expansion of road capacity. The case study shows that the implementation of comprehensive pricing of urban traffic air pollution can reduce traffic pollution and unreasonable traffic flow, which is conducive to the sustainable development of the city.

Case Study of Nanjing Inner Ring on Air Pollution Dispersion from Roadway Tunnel Portals

2012 ◽  
Vol 610-613 ◽  
pp. 1895-1900 ◽  
Author(s):  
Shu Jiang Miao ◽  
Da Fang Fu
Keyword(s):  
Air Pollution ◽  
Pollutant Dispersion ◽  
Air Pollutant ◽  
Urban Traffic ◽  
Lagrangian Model ◽  
Pollution Dispersion ◽  
Piecewise Function ◽  
Air Pollutant Dispersion ◽  
Air Pollution Dispersion

The tunnel module of a rather simple Lagrangian model GRAL (Grazer Langrange model) has been chosen to study air pollutant dispersion around tunnel portals in Nanjing inner ring. Two points have been made to popularize GRAL3.5TM (the tunnel module of a Lagrangian model GRAL; the update was in May 2003) and assure it more suitable for the actual situations in Nanjing. One is to derive a piecewise function of the intermediate parameter ‘stiffness’. Another is to take Romberg NOx-NO2 scheme into account. After these 2 works on GRAL3.5TM, NO2 dispersion from portals of all the 6 tunnels in Nanjing inner ring has been simulated. The importance of limiting urban traffic volume to control air quality around tunnel portals and roadways has been emphasized.

scholarly journals Clean air in the Anthropocene

2017 ◽  
Vol 200 ◽  
pp. 693-703 ◽  
Author(s):  
Jos Lelieveld
Keyword(s):  
Air Pollution ◽  
Sustainable Development ◽  
Air Quality ◽  
Human Health ◽  
Atmospheric Chemistry ◽  
Clean Energy ◽  
Atmospheric Composition ◽  
Anthropogenic Sources ◽  
Years Of Life Lost ◽  
Clean Air

In atmospheric chemistry, interactions between air pollution, the biosphere and human health, often through reaction mixtures from both natural and anthropogenic sources, are of growing interest. Massive pollution emissions in the Anthropocene have transformed atmospheric composition to the extent that biogeochemical cycles, air quality and climate have changed globally and partly profoundly. It is estimated that mortality attributable to outdoor air pollution amounts to 4.33 million individuals per year, associated with 123 million years of life lost. Worldwide, air pollution is the major environmental risk factor to human health, and strict air quality standards have the potential to strongly reduce morbidity and mortality. Preserving clean air should be considered a human right, and is fundamental to many sustainable development goals of the United Nations, such as good health, climate action, sustainable cities, clean energy, and protecting life on land and in the water. It would be appropriate to adopt “clean air” as a sustainable development goal.

scholarly journals Association between Atmospheric Particulate Pollutants and Mortality for Cardio-Cerebrovascular Diseases in Chinese Korean Population: A Case-Crossover Study

2018 ◽  
Vol 15 (12) ◽  
pp. 2835 ◽  
Author(s):  
Chao Zhang ◽  
Zhenyu Quan ◽  
Qincheng Wu ◽  
Zhezhen Jin ◽  
Joseph Lee ◽  
...  
Keyword(s):  
Air Pollution ◽  
Cerebrovascular Disease ◽  
Cerebrovascular Diseases ◽  
Air Pollutant ◽  
Aerodynamic Diameter ◽  
Case Crossover ◽  
Disease Mortality ◽  
Particulate Pollutants ◽  
Pm2.5 And Pm10 ◽  
The Relationship

Background: Air pollution in large Chinese cities has led to recent studies that highlighted the relationship between particulate matters (PM) and elevated risk of cardio-cerebrovascular mortality. However, it is unclear as to whether: (1) The same adverse relations exist in cities with relatively low levels of air pollution; and (2) the relationship between the two are similar across ethnic groups. Methods: We collected data of PM2.5 (PM with an aerodynamic diameter ≤ 2.5 µm) and PM10 (aerodynamic diameter ≤ 10 µm) in the Yanbian Korean Autonomous Prefecture between 1 January 2015 and 31 December 2016. Using a time-stratified case-crossover design, we investigated whether levels of particulate pollutants influence the risk of cardio-cerebrovascular disease mortality among ethnic Korean vs. ethnic Han residents residing in the Yanbian Korean Autonomous Prefecture. Results: Under the single air pollutant model, the odds ratios (ORs) of cardio-cerebrovascular disease were 1.025 (1.024–1.026) for each 10 μg/m3 increase in PM2.5 at lag0 day, 1.012 (1.011–1.013) for each 10 μg/m3 increase in PM10 at lag1 day. In the multi-pollutant model adjusted by PM10, SO2, and NO2, the ORs of cardio-cerebrovascular disease were 1.150 (1.145–1.155) for ethnic Koreans and 1.154 (1.149–1.158) for ethnic Hans for each 10 μg/m3 increase in PM2.5. In the multi-pollutant model adjusted by PM2.5, SO2, and NO2, the ORs of cardio-cerebrovascular disease were 1.050 (1.047–1.053) for ethnic Koreans and 1.041 (1.039–1.043) for ethnic Hans for each 10 μg/m3 increase in PM10. Conclusion: This study showed that PM2.5 and PM10 were associated with increased risks of acute death events in residential cardio-cerebrovascular disease in Yanbian, China.

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