Simulated PM2 Research Articles

Exposure and mortality apportionment of PM2.5 between 2006 and 2015 over the Pearl River Delta region in southern China

Abstract In the early 2000s, the Pearl River Delta (PRD) became one of the earliest regions in China to implement a stringent control policy on air pollution emission. In particular, during the 11th Five Year Plan (FYP) and 12th FYP (i.e., 2006–2015), the emission control measures were executed intensively and efficiently under the supervision of law enforcement authorities. These measures helped substantially reduce the fine particulate matter (PM2.5) concentration over this region. Hence, it is now important to determine how the decrease in PM2.5 concentration influenced the exposure condition and health burdens in the PRD region during 2006–2015. In this study, the exposure and mortality apportionment of PM2.5 under both 2006 and 2015 emission scenarios were investigated. The simulated population-weighted PM2.5 concentration was found to be lower under the 2015 emission scenario compared with that under the 2006 scenario. The average reductions in simulated PM2.5 exposure concentrations (population-weighted average) for Guangzhou, Dongguan, Foshan, and Shenzhen were 32.7, 27.0, 25.3, and 24.1 μg/m3, respectively. After excluding the meteorological variations, a difference of approximately 16,400 (95% CI: 9,100, 22,800) in the number of simulated premature deaths was obtained after the 10-year efforts for emission control and industrial restructuring. Among the five major anthropogenic emissions (mobile, area, power plant, marine vessel, and industrial point emissions), the control of mobile emissions was found to be the most relevant to the estimated health benefits. The calculated economic benefits from controlling mobile emissions reached 30,300 (21,600, 37,100) million USD in 2015. In contrast, the mortality related to area emissions turned out to be higher under the 2015 emission scenario. The difference between 2006 and 2015 meteorological scenarios could substantially influence the simulated exposure concentration in each month. However, the impact of the meteorological factors was found to be limited when the exposure concentrations of the 4 months were averaged. Further, 26,700 (18,500, 33,400) premature deaths difference was calculated after the PM2.5 concentration over the PRD region reached the Air Quality Guideline standard. Therefore, more efforts, including the control of area emissions and the enhancement of regional cooperation (e.g., reducing industrial emission consistently and systematically in southern China), are crucial further to reduce the PM2.5 concentrations in the PRD region and improve the living conditions for the residents.

Climate anomalies contributed to the rebound of PM2.5 in winter 2018 under intensified regional air pollution preventions

Rigorous air pollution managements since 2013 resulted in decreasing trend in fine particulate matter (PM2.5) in China. Regional air pollution prevention measures were extra implemented in the “2 + 26” region since 2017. However, haze pollution dramatically rebounded in the winter of 2018. Both of the observed analyses and the numerical results (basing on a global 3-D chemical transport model) demonstrated that, although intensified prevention measures existed, atmospheric circulation and local meteorological conditions still significantly influenced the variation in haze pollution. The simulated PM2.5 concentrations (with fixed emissions) driven by meteorology in 2018 were 12–15% higher than those with atmospheric circulations in 2017 both under a low and a high emission level, close to the observed 10% PM2.5 rebound. In 2018, positive sea ice anomalies around Beaufort Sea and “triple pattern” anomalies of sea surface temperature in the North Pacific and North Atlantic enhanced the anomalous anticyclonic circulations over the air-polluted region, and thus resulted in minimum surface wind speed during 1979–2018 and 16.8% shallower boundary layer than those in 2017. In the stagnated air of winter 2018, the transported dispersion of pollutant particles was weakened, however more secondary aerosols were produced by enhanced chemical reactions, which jointly contributed to the PM2.5 rebound in 2018.

Meteorological characteristics within boundary layer and its influence on PM2.5 pollution in six cities of North China based on WRF-Chem

North China is recognized as one of the region with most severe air pollution in China, and the PM2.5 in six major cities (Beijing, Tianjin, Shijiazhuang, Taiyuan, Jinan and Zhengzhou) of this region was measured to be 108.7–186.0 μg m−3 in January 2017 and 39.2–56.3 μg m−3 in July 2017. This study conducts the meteorological and chemical simulations based on WRF-Chem, and explores the impacts of meteorology and emissions on PM2.5 (particulate matter with aerodynamic diameters ≤ 2.5 μm) pollutions. Simulations show a noticeable seasonal variation in planetary boundary layer height (PBLH) and relative humidity (RH), but a similar trend in wind speed (WS). Compared to PM2.5 good condition (≤75 μg m−3), daily PBLH decreased by 7.6–39.6% in January and 9.2–44.1% in July during PM2.5 polluted condition (75~150 μg m−3), and by 22.3–51.2% in January during severely polluted condition (≥150 μg m−3). PBLH is thought of as the key factor for PM2.5. Then, the synergy between PBLH and other meteorological factors are studied. For a 100 m failing in PBLH, the decrease in surface WS reaches 0.2–0.8 m s−1 0.1–0.3 m s−1 in January and July. It means a synergic effect of unfavorable horizontal and vertical dispersions, more adversely in January than in July. While, RH presents an increasing trend with PBLH falling, about +2.1~+3.2% in January and +2.7~+4.6% in July per 100 m falling in PBLH. Considering the improvement of high RH in heterogeneous chemistry to form the secondary PM2.5, we believe that the disadvantaged dispersion condition is always accompanied by enhanced secondary PM2.5 formation chemistry. Then, we summarize the average simulated PM2.5 under various combinations of PBLH, WS and RH, and find that under the same meteorological combination, PM2.5 in January was about 1.7–3.6 times that of July, which could be explained by the more emissions of PM2.5 and its precursors in January. Finally, we determine the unfavorable meteorology condition based on current regional emissions and China's standard (hourly PM2.5<75 μg m−3), low PBLH (<600 m) or middle-high RH (>40%) for January, high RH (>60%) & low-middle WS (<4 m·s-1), or middle RH (40~60%) & low PBLH (<400 m) for July. These unfavorable meteorological conditions accounted for 75.8% in January and 47.8% in July. Therefore, it is still necessary to continuously reduce anthropogenic emissions in this region for attainment of China's PM2.5 standards.

Modeling diurnal variation of surface PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; concentrations over East China with WRF-Chem: impacts from boundary-layer mixing and anthropogenic emission

Abstract. Diurnal variation of surface PM2.5 concentration (diurnal PM2.5) could dramatically affect aerosol radiative and health impacts and can also well reflect the physical and chemical mechanisms of air pollution formation and evolution. So far, diurnal PM2.5 and its modeling capability over East China have not been investigated and therefore are examined in this study. Based on the observations, the normalized diurnal amplitude of surface PM2.5 concentrations averaged over East China is weakest (∼1.2) in winter and reaches ∼1.5 in other seasons. The diurnal PM2.5 shows the peak concentration during the night in spring and fall and during the daytime in summer. The simulated diurnal PM2.5 with WRF-Chem and its contributions from multiple physical and chemical processes are examined in the four seasons. The simulated diurnal PM2.5 with WRF-Chem is primarily controlled by planetary boundary layer (PBL) mixing and emission variations and is significantly overestimated against the observation during the night. This modeling bias is likely primarily due to the inefficient PBL mixing of primary PM2.5 during the night. The simulated diurnal PM2.5 is sensitive to the PBL schemes and vertical-layer configurations with WRF-Chem. Besides the PBL height, the PBL mixing coefficient is also found to be the critical factor determining the PBL mixing of pollutants in WRF-Chem. With reasonable PBL height, the increase in the lower limit of the PBL mixing coefficient during the night can significantly reduce the modeling biases in diurnal PM2.5 and also the mean concentrations, particularly in the major cities of East China. It can also reduce the modeling sensitivity to the PBL vertical-layer configurations. The diurnal variation and injection height of anthropogenic emissions also play roles in simulating diurnal PM2.5, but the impact is relatively smaller than that from the PBL mixing. This study underscores that more efforts are needed to improve the boundary mixing process of pollutants in models with observations of PBL structure and mixing fluxes in addition to PBL height, in order to simulate reasonably the diurnal PM2.5 over East China. The diurnal variation and injection height of anthropogenic emissions must also be included to simulate the diurnal PM2.5 over East China.

Street-scale air quality modelling for Beijing during a winter 2016 measurement campaign

Abstract. We examine the street-scale variation of NOx, NO2, O3 and PM2.5 concentrations in Beijing during the Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China) winter measurement campaign in November–December 2016. Simulations are performed using the urban air pollution dispersion and chemistry model ADMS-Urban and an explicit network of road source emissions. Two versions of the gridded Multi-resolution Emission Inventory for China (MEIC v1.3) are used: the standard MEIC v1.3 emissions and an optimised version, both at 3 km resolution. We construct a new traffic emissions inventory by apportioning the transport sector onto a detailed spatial road map. Agreement between mean simulated and measured pollutant concentrations from Beijing's air quality monitoring network and the Institute of Atmospheric Physics (IAP) field site is improved when using the optimised emissions inventory. The inclusion of fast NOx–O3 chemistry and explicit traffic emissions enables the sharp concentration gradients adjacent to major roads to be resolved with the model. However, NO2 concentrations are overestimated close to roads, likely due to the assumption of uniform traffic activity across the study domain. Differences between measured and simulated diurnal NO2 cycles suggest that an additional evening NOx emission source, likely related to heavy-duty diesel trucks, is not fully accounted for in the emissions inventory. Overestimates in simulated early evening NO2 are reduced by delaying the formation of stable boundary layer conditions in the model to replicate Beijing's urban heat island. The simulated campaign period mean PM2.5 concentration range across the monitoring network (∼15 µg m−3) is much lower than the measured range (∼40 µg m−3). This is likely a consequence of insufficient PM2.5 emissions and spatial variability, neglect of explicit point sources, and assumption of a homogeneous background PM2.5 level. Sensitivity studies highlight that the use of explicit road source emissions, modified diurnal emission profiles, and inclusion of urban heat island effects permit closer agreement between simulated and measured NO2 concentrations. This work lays the foundations for future studies of human exposure to ambient air pollution across complex urban areas, with the APHH-China campaign measurements providing a valuable means of evaluating the impact of key processes on street-scale air quality.

Random forest models for PM2.5 speciation concentrations using MISR fractional AODs

It is increasingly recognized that various chemical components of PM2.5 might have differential toxicities to human health, although such studies are hindered by the sparse or non-existent coverage of ground PM2.5 speciation monitors. The Multi-angle Imaging SpectroRadiometer (MISR) onboard the Terra satellite has an innovative design to provide information about aerosol shape, size and extinction that are more related to PM2.5 speciation concentrations. In this study, we developed random forest models that incorporated ground measurements of PM2.5 species, MISR fractional AODs, simulated PM2.5 speciation concentrations from a chemical transport model (CTM), land use variables and meteorological fields, to predict ground-level daily PM2.5 sulfate, nitrate, organic carbon (OC) and elemental carbon (EC) concentrations in California between 2005 and 2014. Our models had out-of-bag R2 of 0.72, 0.70, 0.68 and 0.70 for sulfate, nitrate, OC and EC, respectively. We also conducted sensitivity tests to explore the influence of variable selection on model performance. Results show that if there are sufficient ground measurements and predictor data to support the most sophisticated model structure, fractional AODs and total AOD have similar predicting power in estimating PM2.5 species. Otherwise, models using fractional AODs outperform those with total AOD. PM2.5 speciation concentrations are more sensitive to land use variables than other supporting data (e.g., CTM simulations and meteorological information).

Model Inter-Comparison for PM2.5 Components over urban Areas in Japan in the J-STREAM Framework

A model inter-comparison of secondary pollutant simulations over urban areas in Japan, the first phase of Japan’s study for reference air quality modeling (J-STREAM Phase I), was conducted using 32 model settings. Simulated hourly concentrations of nitric oxide (NO) and nitrogen dioxide (NO2), which are primary pollutant precursors of particulate matter with a diameter of 2.5 µm or less (PM2.5), showed good agreement with the observed concentrations, but most of the simulated hourly sulfur oxide (SO2) concentrations were much higher than the observations. Simulated concentrations of PM2.5 and its components were compared to daily observed concentrations by using the filter pack method at selected ambient air pollution monitoring stations (AAPMSs) for each season. In general, most models showed good agreement with the observed total PM2.5 mass concentration levels in each season and provided goal or criteria levels of model ensemble statistics in warmer seasons. The good performances of these models were associated with the simulated reproducibility of some dominant components, sulfates (SO42−) and ammonium (NH4+). The other simulated PM2.5 components, i.e., nitrates (NO3−), elemental carbon (EC), and organic carbon (OC), often show clear deviations from the observations. The considerable underestimations (approximately 30 µg/m3 for total PM2.5) of all participant models found on heavily polluted days with approximately 40–50 µg/m3 for total PM2.5 indicated some problems in the simulated local meteorology such as the atmospheric stability. This model inter-comparison suggests that these deviations may be owing to a need for further improvements both in the emission inventories and additional formation pathways in chemical transport models, and meteorological conditions also require improvement to simulate elevated atmospheric pollutants. Additional accumulated observations are likely needed to further evaluate the simulated concentrations and improve the model performance.

Diagnostic analysis of wintertime PM2.5 pollution in the North China Plain: The impacts of regional transport and atmospheric boundary layer variation

Air pollution has recently become a regional problem in the North China Plain, which is heavily populated with industrialized city clusters. The local air quality is frequently affected by air pollutant transport in this region, as well as other meteorological conditions. This study aims to reveal the roles of air pollutant transport and atmospheric boundary layer variation in the development of air pollution episodes. Two cities (Dezhou and Cangzhou in Shandong and Hebei Provinces, respectively) near the centre of the North China Plain are chosen, and PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations over two winters (January 2016 and December 2017 to January 2018) are evaluated. Both meteorological and air quality observational data are used to construct hourly wind and PM2.5 concentration fields. High-quality atmospheric boundary layer soundings provide detailed information on boundary layer height and its diurnal evolution. A simple box model is employed to simulate variation in PM2.5 levels in these two cities by using the spatial gradients and temporal variation rates in the diagnostic fields of wind and PM2.5 concentrations. Two prominent factors affecting PM2.5 concentration variation are identified by comparing the simulated and observed diurnal variation in PM2.5 concentration. The first one is the entrainment of upper-layer air pollutants into the boundary layer in the morning. And the second one is the temporary accumulation of PM2.5 during the day-to-evening period. After incorporating these two effects, the simulated PM2.5 is comparable with the observed data. The correlation coefficients between the simulation and observation are 0.84 and 0.77 (P < 0.01) for Cangzhou and Dezhou, respectively, and the normalized mean biases are −0.04 and −0.09, respectively. The relative contributions of different processes affecting PM2.5 concentrations during air pollution episodes are assessed with the model simulations. The percentage contributions for local emissions, deposition, horizontal advection, temporary accumulation, and boundary layer height variation are from 27% to 45%, −28% to −20%, 46% to 76%, 13% to 40%, and −6% to 22%, respectively. Local emission and regional air pollutant transport are the main causes of these episodes. Temporary accumulation of PM2.5 during the day-to-evening period is also an important contributor to the episodes. In addition, the entrainment shows varying contributions to PM2.5 increase with a maximum rate of 30% during the PM2.5 pollution episodes in the North China Plain.

Validation of PM2.5 model particle through physicochemical evaluation and atherosclerotic plaque formation in ApoE-/- mice.

PM2.5 particles are regarded as prominent risk factors that contribute to the development of atherosclerosis. However, the composition of PM2.5 is rather complicated. This study aimed to provide a model particle that simulates the behavior of actual PM2.5, for subsequent use in exploring mechanisms and major complications arising from PM2.5. To establish model particles of PM2.5, a series of monodisperse SiO2 microspheres with different average grain diameters were mixed according to the size distribution of actual PM2.5. The organic carbon (OC) was removed from PM2.5 and coated onto the SiO2 model particle, to formulate simulant PM2.5. Results showed that the size distribution of the model particle was highly approximate to that of the PM2.5 core. The polycyclic aromatic hydrocarbon (PAHs) composition profile of the simulated PM2.5 were approximate to PM2.5, and loading efficiency was approximately 80%-120%. Furthermore, compared to the control, SiO2-only model particle had negligible cytotoxicity on cell viability and oxidative stress of HUVECs, and marginal effect on the lipid metabolism and atherosclerotic plaque formation in ApoE-/- mice. In contrast, simulated PM2.5 exhibited similar cytotoxic and detrimental effects on lipid metabolism and atherosclerotic plaque formation with actual PM2.5. Traffic-related PM2.5 had negative effects on endothelial function and led to the formation of atherosclerosis via oxidative stress. The simulated PM2.5 simulated the outcomes of actual PM2.5 exposure. Here, we show that SiO2 particle model cores coated with OC could significantly assist in the evaluation of the effects of specific organic compositions bound on PM2.5, specifically in the context of environmental health and safety.

Impacts of Biomass Burning Emission Inventories and Atmospheric Reanalyses on Simulated PM10 over Indochina

Biomass burning (BB) is a major source of atmospheric particles over Indochina during the dry season. Moreover, Indochina has convoluted meteorological scales, and regional meteorological conditions dominate the transport patterns of pollutants. This study focused on the impacts of BB emission inventories and atmospheric reanalyses on simulated PM10 over Indochina in 2014 using the Community Multiscale Air Quality (CMAQ) model. Meteorological fields to input to CMAQ were produced by using the Weather Research and Forecasting (WRF) model simulation with the United States National Centers for Environmental Prediction Final (NCEP FNL) Operational Global Analysis or European Centre for Medium Range Weather Forecasts Interim Reanalysis (ERA-interim). The Fire INventory from NCAR (FINN) v1.5 or the Global Fire Emissions Database including small fires (GFED v4.1s) was selected for BB emissions for the air quality simulation. The simulation case with NCEP FNL and FINN v1.5 (FNL + FINN) performed best throughout 2014, including the season when BB activities were intensified. The normalized percentage difference for maximum daily mean PM10 concentrations at Chiang Mai for FNL + FINN and the two simulation cases applying GFED v4.1s for BB emissions (−53% to −27%) was much larger than that between the FNL + FINN and ERA + FINN cases (10%). BB emission inventories more strongly impacted PM10 simulation than atmospheric reanalyses in highly polluted areas by BB over Indochina in 2014.

Optimal Inversion of Conversion Parameters from Satellite AOD to Ground Aerosol Extinction Coefficient Using Automatic Differentiation

Satellite aerosol optical depth (AOD) plays an important role for high spatiotemporal-resolution estimation of fine particulate matter with diameters ≤2.5 μm (PM2.5). However, the MODIS sensors aboard the Terra and Aqua satellites mainly measure column (integrated) AOD using the aerosol (extinction) coefficient integrated over all altitudes in the atmosphere, and column AOD is less related to PM2.5 than low-level or ground-based aerosol (extinction) coefficient (GAC). With recent development of automatic differentiation (AD) that has been widely applied in deep learning, a method using AD to find optimal solution of conversion parameters from column AOD to the simulated GAC is presented. Based on the computational graph, AD has considerably improved the efficiency in applying gradient descent to find the optimal solution for complex problems involving multiple parameters and spatiotemporal factors. In a case study of the Jing-Jin-Ji region of China for the estimation of PM2.5 in 2015 using the Multiangle Implementation of Atmospheric Correction AOD, the optimal solution of the conversion parameters was obtained using AD and the loss function of mean square error. This solution fairly modestly improved the Pearson’s correlation between simulated GAC and PM2.5 up to 0.58 (test R2: 0.33), in comparison with three existing methods. In the downstream validation, the simulated GACs were used to reliably estimate PM2.5, considerably improving test R2 up to 0.90 and achieving consistent match for GAC and PM2.5 in their spatial distribution and seasonal variations. With the availability of the AD tool, the proposed method can be generalized to the inversion of other similar conversion parameters in remote sensing.

High-Resolution Biomass Burning Aerosol Transport Simulations in the Tropics

This study evaluates the performance of the Weather Research and Forecasting Model with Chemistry (WRF-Chem) for simulating biomass burning aerosol transport at high resolution in the tropics using two different biomass burning emission inventories. Hourly, daily, and monthly average PM10 dry mass concentrations at 5 km resolution—simulated separately using the Brazilian Biomass Burning Emission Model (WRF-3BEM) and the Fire Inventory from NCAR (WRF-FINN) and their averages (WRF-AVG) for 3 months from February to April—are evaluated, using measurements from ground stations distributed in northern Thailand for 2014 and 2015. Results show that WRF-3BEM agrees well with observations and performs much better than WRF-FINN and WRF-AVG. WRF-3BEM simulations are almost unbiased, while those of WRF-FINN and WRF-AVG are significantly overestimated due to significant overestimates of FINN emissions. WRF-3BEM and the measured monthly average PM10 concentrations for all stations and both years are 89.22 and 87.20 μg m−3, respectively. The root mean squared error of WRF-3BEM simulated monthly average PM10 concentrations is 72.00 and 47.01% less than those of WRF-FINN and WRF-AVG, respectively. The correlation coefficient of WRF-3BEM simulated monthly PM10 concentrations and measurements is 0.89. WRF-3BEM can provide useful biomass burning aerosol transport simulations for the northern region of Thailand.

Model-simulated Source Contributions to PM2.5 in Santiago and the Central Region of Chile

The contributions to PM2.5 from different emission sectors across central Chile and the Santiago metropolitan area during summer/fall and winter have been evaluated using a chemical transport model. The simulations generally underestimate the mean PM2.5 concentrations compared to measurements conducted at stations in Santiago that belong to the Chilean National Air Quality Information System (SINCA). The potential reasons for this discrepancy include underestimated direct PM2.5 emissions, missing emissions for semi- and intermediately volatile organic compounds (SVOCs and IVOCs) and overestimated wind speeds in the simulations. The simulated winter PM2.5 concentrations in Santiago are lower and higher than the values observed during nighttime, and daytime and late evening, respectively, which may be related to excessive simulated wind speeds, as well as to uncertainties in the diurnal variation in the emissions. During summer/fall, the simulated diurnal variation better agrees with the observations, but the peak concentrations during the morning are underestimated, whereas those during the evening are overestimated. The simulated contributions of different aerosol components to the PM2.5 at one station in Santiago are all lower than the observed values, except for elemental carbon equivalent black carbon (BCe) , which exhibit comparable or higher levels in the simulations. The absolute differences are the largest for the total organic matter, whereas the relative differences are the largest for BCe and ammonium. The simulated sector contributions indicate that emissions originating from transport and construction machinery dominate the PM2.5 in Santiago; however, residential wood combustion is the primary source in other urban areas of central Chile, except near major point sources. Away from urban areas, traffic routes and major industrial sources, secondary inorganic aerosol (SIA) is estimated to be the largest component of the aerosol, whereas the simulated secondary organic aerosol (SOA) only contributes a small fraction.

Influence of the Anthropogenic Fugitive, Combustion, and Industrial Dust on Winter Air Quality in East Asia

We estimate the effects of the anthropogenic fugitive, combustion, and industrial dust (AFCID) on winter air quality in China and South Korea for November 2015–March 2016 using the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (KU-CREATE) monthly anthropogenic emission inventory in conjunction with a nested version of GEOS-Chem. Including AFCID emissions in models results in a better agreement with observations and a reduced normalized mean bias of −28% compared to −40% without AFCID. Furthermore, we find that AFCID amounts to winter PM10 concentrations of 17.9 μg m−3 (17%) in eastern China (30−40° N, 112−120° E) with the largest contribution of AFCID to winter PM10 concentrations of up to 45 μg m−3 occurring in eastern China causing a significant impact on air quality to downwind regions. Including AFCID in the model results in an increase of simulated winter PM10 concentrations in South Korea by 3.1 μg m−3 (9%), of which transboundary transport from China accounts for more than 70% of this increased PM10 concentration. Our results indicate that AFCID is an essential factor for winter PM10 concentrations over East Asia and its sources and physical characteristics need to be better quantified to improve PM air quality forecasts.

Separating Emission and Meteorological Drivers ofMid‐21st‐Century Air Quality Changes in IndiaBased on Multiyear Global‐RegionalChemistry‐Climate Simulations

AbstractMany Indian metropolitan areas currently suffer from severe air pollution such as PM2.5, which might continue into future decades, dependent on the trends in emission growth and regional climate. Based on a multiyear Nested Regional Climate Model coupled with Chemistry simulation, we developed a daily index (Hazy Weather Index for India, HWII) to characterize the meteorology‐pollution relationship over three heavily polluted cities (Delhi, Kolkata, and Mumbai) and Indo‐Gangetic Plain. HWII consists of near‐surface (10 m) zonal wind (U10) and temperature at 200 hPa (T200) over the northwestern Indian Ocean, and local planetary boundary layer height. The simulated PM2.5 levels during the Historical Period (1997–2004) exhibit robust negative correlation with the HWII. The negative correlation captures day‐to‐day covariability of surface PM2.5 and meteorology, highlighting the role of monsoon‐related large‐scale circulation in redistributing locally emitted pollutants. More importantly, two future (2046–2054) simulations with regional warming under the Representative Concentration Pathway 6.0 and 8.5 were analyzed. The future changes in HWII and the three predictive meteorological variables work in favor of a stronger pollution horizontal dispersion and vertical ventilation and thus could lead to a reduction of PM2.5 level by 7%. The meteorology‐driven reduction in PM2.5, however, is overwhelmed by the projected growth in anthropogenic emission (especially under Representative Concentration Pathway 8.5 emission by 31%). Our results are contrary to previous studies over other regions (e.g., China) where future climate change might contribute to PM2.5 increase.

A comparative assessment of regional representativeness of EDGAR and ECLIPSE emission inventories for air quality studies in India

Ambient PM2.5 (fine particulate matter) exposure is one of the leading health risk factors in India. The role of chemical transport models (CTMs) in air quality management is well recognized today. However, effectiveness of CTM outputs critically depends on accuracy of emission inventory. Here we evaluate anthropogenic PM2.5 simulated by WRF-Chem using two major inventories - EDGAR and ECLIPSE with satellite and MERRA-2 reanalysis data. We found that both inventories show similar accuracy statistically (correlation coefficients, R of 0.87 and 0.9 and RMSEs of 8.4 and 10.2 μg/m³ for EDGAR and ECLIPSE, respectively) at national scale, but with significant regional variation. The simulated anthropogenic populated PM2.5 exposure is high with ECLIPSE emission compare to EDGAR for most of the states. In some populated states Delhi, Uttar Pradesh, Haryana, Bihar, Uttarakhand, Rajasthan, Punjab and few less populated states Sikkim, Tripura exposure is exceeding more than 30% with ECLIPSE emission inventory compare to EDGAR. After the monsoon is withdrawn, pollution builds up in the western Indo-Gangetic Basin (IGB), while it is pushed towards eastern IGB in the winter season. Diurnal amplitude in anthropogenic PM2.5 exceeds 15 μg/m3 in large parts of India especially during the post-monsoon to winter season suggesting that exposure modeling needs to be carried out with higher temporal frequency for better health burden assessment.

Numerical study of air pollution over a typical basin topography: Source appointment of fine particulate matter during one severe haze in the megacity Xi'an

Many cities are located in lands with typical basin topographies, which are not conducive to the spread of air pollutants. In the winter of 2016/2017, a severe haze happened in Xi’an, the main city in the Guanzhong Basin in central China. When the peak daily concentration of fine particulate matter (PM2.5) reaches 499 μg/m3, the source of the atmospheric pollution needs to be found urgently in order to take countermeasures. The comprehensive air quality model with extensions, coupled with the tracer tagging particulate source apportionment technology (PSAT) module, and an improved emission inventory, higher grid resolution, and bigger inner domain area, have been applied to quantify the contributions of local and regional emissions to the PM2.5 pollutions. The model performed well in time period considered in this study. The correlation of the simulated daily PM2.5 concentration data reaches 0.82, and the fraction of predictions within a factor of two of observations approaches 84%. With the PSAT module, the PM2.5 contributions from local and regional sources to the urban centre and rural areas during the severe winter haze event are analysed in detail. The PM2.5 concentrations in the urban centre in Xi’an is mainly originating from local emissions (60%), and Xianyang City is the largest contributor among the surrounding source regions (11.6%), while the transportation sector outside the Shaanxi Province (5.1%) also contributes significantly. Comparatively, the rural areas have lower local contributions and higher transport contributions. In particular, in the northern rural area Yanliang, the contribution from surrounding source regions approaches 82%. The results of this study suggest that to improve the air quality in a typical basin city, a regional-scale coordinated emissions control should be used, focusing on the emissions from both local and surrounding areas.

Energy and emission pathways towards PM2.5 air quality attainment in the Beijing-Tianjin-Hebei region by 2030.

In 2013, the Chinese government announced its first air quality standard for PM2.5 (particulate matter with a diameter < 2.5 μm) which requires annual mean PM2.5 concentration to achieve the World Health Organization (WHO) interim target 1 of 35 μg/m3 nationwide including the most polluted region of Beijing-Tianjin-Hebei (BTH). Here, we explore the future mitigation pathways for the BTH region to investigate the possibility of air quality attainment by 2030 in that region, by developing two energy scenarios (i.e., baseline energy scenario and enhanced energy scenario) and two end-of-pipe scenarios (i.e., business as usual scenario and best available technology scenario) and simulating future air quality for different scenarios using the WRF/CMAQ model. Results showed that without stringent energy and industrial structure adjustment, even the most advanced end-of-pipe technologies did not allow the BTH region to attain the 35 μg/m3 target. Under the most stringent scenario that coupled the enhanced structure adjustment measures and the best available end-of-pipe measures, the emissions of SO2, NOx, PM2.5 and NMVOCs (nonmethane volatile organic compounds) were estimated to be reduced by 85%, 74%, 82% and 72%, respectively, in 2030 over the BTH region. As a result, the simulated annual mean PM2.5 concentrations in Beijing, Tianjin and Hebei could decline to 23, 28 and 28 μg/m3, respectively, all of which achieved the 35 μg/m3 target by 2030. Our study identified a feasible pathway to achieve the 2030 target and highlighted the importance of reshaping the energy and industrial structure of the BTH region for future air pollution mitigation.

Natural Variability in Projections of Climate Change Impacts on Fine Particulate Matter Pollution

AbstractVariations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model‐based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic‐induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic‐forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate‐induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.

The transport of PM10 over Cape Town during high pollution episodes

This study investigates the influence of PM10 from remote sources on PM10 episodes over the city of Cape Town. For the study, we analysed observation data from Cape Town's air quality monitoring stations as well as high-resolution simulation data from the Weather Research and Forecasting model with Chemistry (WRF-Chem). The observation data were used to identify PM10 episodes during the study period (2008–2014) and WRF-Chem was applied to simulate the atmospheric conditions and PM10 transport over southern Africa during each episode. The capability of WRF-Chem to simulate the wind and PM10 concentration over Cape Town was quantified and the paths of air parcels over Cape Town during each episode were tracked. Results of the study show that WRF-Chem gives a realistic simulation of observed wind (speed and direction) over the city during the episodes, but the model struggles to reproduce the observed PM10 concentration. For most episodes, the magnitude of the simulated PM10 is lower than the observed due to lack of local emissions in the simulations. In some cases, the model reproduces the peak in PM10 concentration some days earlier or later than observed. The simulations show that most air parcels over Cape Town during the episodes have travelled over major dust source regions (i.e., the Kalahari or Namib Desert) before reaching the city. Most of episodes are associated with a southward transport of a plume of PM10 from the north-west coast of southern Africa to Cape Town. This PM10 plume is induced by a coastal trough and a continental high pressure system. In some cases, local topography influences the intrusion of the PM10 plume into Cape Town by blocking some of the pollution, thereby minimising the amount of PM10 that reaches the city. Results of the study suggest that the transport of PM10 from the north-west coast of southern Africa may contribute to PM10 episodes in Cape Town.