Sources Of PM2 Research Articles

Development of 2015 Vietnam emission inventory for power generation units

The 2010 and 2015 Emission Inventories (EIs) for Vietnam Thermal Power Plants (TPP) were concurrently developed using the bottom-up approach. Local activity data, emission factors, and pollution control efficiencies were applied to estimate the annual emissions of individual power plants. As the EIs consider all emission control activities with relevant unit upgrades, the estimated SO2, NOx and PM species were found to be lower than most of the reported EIs by 20–70%. An overestimation (i.e., 3.8 times) of SO2 has been observed in Emission Database for Global Atmospheric Research (EDGAR) EI, which can possibly be attributed to the assumption of no air pollution controls in their inventory. Overall, the newly developed EIs indicate that the annual emissions of coal-fired TPPs were highest among the various types of TPPs, and the largest uncertainty occurred in NOx with ranges between −23% and +31%. In terms of regional distribution, the most significant emission sources of CO2, N2O, SO2, NOx, PM10, PM2.5, BC, and OC were from the Red River Delta region resulted from coal-fired TPPs, while the Southeast region had the largest sources of CO, NMVOC, and CH4 caused by gas-fired TPPs. The study reveals that from 2010 to 2015, the growths of NOx, CO, NMVOC, PM10, PM2.5, BC, OC, CO2, CH4, and N2O emissions were 51%, 39%, 41%, 109%, 88%, 9%, 107%, 58%, 33%, and 119%, respectively, while ~19% reduction in SO2 was found resulted from the decommission of oil-fired TPPs.

Comparison of PM2.5 exposure estimates in the REGARDS cohort: understanding differences by community type and exposure assignment choices

Background: There is biologic rationale for associations between ambient PM2.5 exposure and type 2 diabetes. However, studies of these associations demonstrate mixed results, potentially due to differences in: PM2.5 estimation by community types and regions; PM2.5 estimation methods that optimize temporal vs. spatial variability; and exposure lags and durations assigned to individuals for evaluation with diabetes outcomes. Methods: We evaluated several PM2.5 data sources and exposure assignment choices for 10,332 participants free of diabetes at baseline (2003-2007) and with follow-up data on diabetes in the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort. We calculated exposure durations of 2-weeks, 30-days, and 1-year; lagged 1-day, 6-months, and 1-year prior to baseline, and evaluated these by: US region (Northeast, South, Midwest, and West); community type (high density urban, low density urban, suburban and small town, and rural); and year for two sources of PM2.5: CDC EPA Downscaler model and CDC Wonder data modeled from NASA satellite observations and EPA monitor data. Results: Participants in the analysis had a mean (SD) age of 63.0 (8.5) years, were 55.8% female; 32.4% black. The mean (SD) PM2.5 estimates from CDC Wonder were 13.5 (4.2) µg/m3, 13.5 (3.6) µg/m3, and 13.3 (2.0) µg/m3, for 2-week, 30-day, and 1-year exposure periods, respectively. One-way analysis of variance (ANOVA) of PM2.5 exposure estimates of all durations showed significant differences (p ≤ 0.01) by community type, region, and year. Differences by region and community type became more pronounced with longer exposure durations. Analysis of additional exposure lags and durations is in progress. Conclusions: These results suggest that exposure assignment choices can either exacerbate or mitigate underlying spatial differences in this cohort, which can lead to differential associations between PM2.5 and diabetes. Future work should focus on better exposure classification in order to more clearly estimate this association.

Occupational exposure to fine particulate matter (PM4 and PM2.5) during artisanal cookware manufacturing: personal, indoor and outdoor levels

Background: Occupational exposure to fine particulate matter is associated with adverse respiratory, cardiovascular and pregnancy outcomes. Artisanal cookware manufacturing involves preparing sand molds for casting and a smelting process to cast liquid aluminium melted from a collection of scrap metal. Objective: The aim of this study was to characterize occupational exposure to fine particulate matter (PM4 and PM2.5) during artisanal cookware manufacturing, and to examine the relationship between personal, indoor and outdoor PM levels. Methods: Exposure to fine particulate matter was measured for 17 male participants across five artisanal cookware manufacturing sites during the winter season. SidePak personal aerosol monitors (AM520) were used to measure personal exposure to PM4, while a DustTrak monitor and an E-sampler were used to assess indoor and outdoor PM2.5 levels, respectively. Personal measurements were undertaken for a period of 3 hours per participant. Indoor and outdoor sampling was performed for a period of 8 hours for each site. A questionnaire was administered face-to-face by field workers to capture information on demographic characteristics. Spearman’s correlation coefficient between personal, indoor and outdoor levels was used. Time series record of 15-min averages for indoor and outdoor PM2.5 levels were assessed. Results: Personal PM exposure levels at breathing zones of workers exceeded environmental levels. The mean (SD) was 490 µg/m3 (3520), 150 µg/m3 (390) and 20 µg/m3 (50), respectively for personal PM4, indoor and outdoor PM2.5. Time series analysis showed higher indoor than outdoor PM levels with little evidence of elevated outdoor PM sources. In most cases, there were somewhat low correlations between personal, indoor and outdoor PM measurements. Correlation was higher between PM and meteorological factors in some of the sites. Conclusions: The levels of fine particulate exposure among artisanal cookware makers were elevated, especially in respect of personal breathing zone measurements.

Modelling daily PM10 concentrations at high spatio-temporal resolution across South Africa.

Background Air pollution is a major environmental risk to health, responsible for one in every nine deaths globally. Currently, limited spatiotemporal air pollution data is available to conduct large-scale epidemiological studies investigating the adverse health effects of air pollution in South Africa. Here we aim to model South Africa’s daily average PM10 concentrations for years 2010 to 2016 using spatial and temporal predictor variables including South Africa’s data from Moderate Resolution Imaging Spectroradiometer (MODIS) and Copernicus Atmosphere Monitoring Service (CAMS). Methods We followed a two-stage modelling strategy. In stage 1, we imputed missing Aerosol Optical Depth (AOD) data, due mainly to cloud cover, by developing Random Forest (RF) models between AOD and CAMS AOD estimates also including additional large-scale spatiotemporal predictors. In stage 2, we developed RF models to explain the measured PM10 concentration using the imputed AOD data from stage 1 and spatiotemporal predictors including land cover, road density, population density, altitude, climatological zones, and meteorological variables. Results Preliminary analysis for 2016 shows that our stage 1 RF models can explain 96 percent in variability in the AOD data. In stage 2, our models explain 79% of the variation in measured ground level PM10 concentrations with the corresponding 10-fold cross-validation R2 of 78%. The RF stage 2 model includes variables depicting some PM10 source related variables like day of the week, geographical coordinates, road density, altitude and meteorological predictor variables in the top 10 of influential predictor variables. Conclusion This is the first study showing the potential of earth observation data to develop models explaining fine scale (1x1 km) temporal (daily) variation of PM10 across South Africa. We developed models for years 2010 to 2016. The predicted PM10 concentrations will be made available to facilitate health research in South Africa.

Chemical characteristics and sources of ambient PM2.5 in a harbor area: Quantification of health risks to workers from source-specific selected toxic elements.

Samples of ambient PM2.5 were collected in the Qingdao harbor area between 21 March and May 25, 2016, and analyzed to investigate the compositions and sources of PM2.5 and to assess source-specific selected toxic element health risks to workers via a combination of positive matrix factorization (PMF) and health risk (HR) assessment models. The mean concentration of PM2.5 in harbor area was 48μgm-3 with organic matter (OM) dominating its mass. Zn and V concentrations were significantly higher than the other selected toxic elements. The hazard index (HI) and cancer risk (Ri) of all selected toxic elements were lower than the United States Environmental Protection Agency (USEPA) limits. There were no non-cancer and cancer risks for workers in harbor area. The contributions from industrial emissions (IE), ship emissions (SE), vehicle emissions (VE), and crustal dust and coal combustion (CDCC) to selected toxic elements were 39.0%, 12.8%, 24.0%, and 23.0%, respectively. The HI values of selected toxic elements from IE, CDCC, SE, and VE were 1.85×10-1, 7.08×10-2, 6.36×10-2, and 3.37×10-2, respectively; these are lower than the USEPA limits. The total cancer risk (Rt) value from selected toxic elements in CDCC was 2.04×10-7, followed by IE (6.40×10-8), SE (2.26×10-8), and VE (2.18×10-8). CDCC and IE were the likely sources of cancer risk in harbor area. The Bo Sea and coast were identified as the likely source areas for health risks from IE via potential source contribution function (PSCF) analysis based on the results of PMF-HR modelling. Although the source-specific health risks were below the recommended limit values, this work illustrates how toxic species in PM2.5 health risks can be associated with sources such that control measures could be undertaken if the risks warranted it.

Chemical characterization and source apportionment of fine and coarse atmospheric particulate matter in Doha, Qatar

Atmospheric particulate matter of two size fractions (i.e., PM2.5 and PM10) were sampled simultaneously at an urban site in Doha, Qatar, and characterized to measure chemical constituents (including trace elements, organic and elemental carbon (OC/EC), and water-soluble ions). The composition data were applied to mass closure analysis and Positive Matrix Factorization (PMF) model along with detailed wind direction analysis for quantifying and identifying the probable sources of both PM size fractions. The average concentrations of PM2.5 (40 μg m−3) and PM10 (146 μg m−3) were greatly exceeded the WHO air quality thresholds for 78–92% of the sampling days. Mass closure indicated the predominance of secondary inorganic aerosol (39%) and EC (7%) in fine PM, while mineral dust contributes 50% to the total mass of coarse particles. PMF identified six major sources of fine and coarse PM, including secondary inorganic aerosols, vehicular emissions, heavy oil combustion, marine aerosols, mineral and fugitive dust. The wind direction analysis indicated that both local and regional sources impact Qatar's air quality attributed to specific wind conditions. Two main sources of heavy oil combustion and secondary aerosols are closely related to regional emissions from shipping vessels and the petrochemical industry in the Gulf region. The desert dust transport mixed with local re-suspended dust and sea-salt particles also contributes considerably to the local PM concentrations. The significant contributions of regional anthropogenic and natural emissions pose a significant challenge in meeting local PM standards, signifying the importance of regional collaboration for better control policies on reducing transboundary air pollution.

Chemical nature and predominant sources of PM10 and PM2.5 from multiple sites on the Silk Road, Northwest China

We analyzed particulate matter (PM10 and PM2.5) from 4 urban sites (Wuwei, Zhangye, Jiuquan and Dunhuang) located on the Silk Road of Northwest China in summer and winter of 2018 for water-soluble ions, carbonaceous aerosols and heavy metals. The focus was to examine patterns in mass concentration, chemical nature and predominant sources. The average concentrations of PM10 and PM2.5 in this region were in the range of 112–152 μg/m3 and 70–81 μg/m3, respectively, and water-soluble inorganic ions (WSIIs) and mineral dust were the two main species of PM. PM10 and PM2.5 showed a relatively homogeneous spatial distribution in this area. The ratios of chemical composition and the enrichment factors of heavy metals were also analyzed. Four sources were identified by the positive matrix factorization (PMF) model. Dust and secondary inorganic aerosols were the dominant PM10 and PM2.5 contributors, respectively, accounting for 32.2% for PM10 and 34.7% for PM2.5. The contribution of dust and factory combustion emission sources to PM2.5 in this region was higher than that in the North, East and South regions of China, while that of secondary inorganic aerosol sources was relatively lower than in these other areas. The backward-trajectory model revealed that high PM pollution was related to the air masses from adjacent polluted regions in Inner Mongolia and was characterized by high PM contributions from secondary aerosols.

Light absorption enhancement of particulate matters and their source apportionment over the Asian continental outflow site and South Yellow Sea.

Light absorption enhancement of black carbon due to the aerosol mixing states is an important parameterization for climate modeling, while emission source contributions to the enhancement factor are unclear. An intensive campaign was conducted simultaneously at a China coastal site (Qingdao city) and maritime sites (South Yellow Sea, SYS) in August and Nov to Dec 2018. The absorption enhancement (EMAC) of the black carbon was calculated using a two-step solvent dissolution protocol and found 1.96 ± 0.68, 1.64 ± 0.38, and 2.40 ± 0.76 for Qingdao summer (QS), Qingdao autumn (QA), and SYS, respectively. Positive matrix factorization (PMF) model identified six sources of PM2.5 and EMAC, which were secondary aerosol (with contribution 27.9% and 29.2%), coal combustion (24.9% and 20.2%), industrial emissions (15.2% and 25.4%), sea salt (6.9% and 9.6%), vehicle emissions (12.1% and 10.9%), and soil dust (13.0% and 4.7%), respectively. These sources increased the absorption of black carbon by a factor of 1.25 ± 0.11 (secondary aerosol), 1.21 ± 0.20 (industrial emissions), 1.17 ± 0.08 (coal combustion), 1.09 ± 0.07 (vehicle emissions), 1.08 ± 0.17 (sea salt), and 1.04 ± 0.10 (soil dust). Based on the correlation between PM and EMAC source contributions, we estimated that secondary aerosols, industrial emissions, and coal combustion contributed to 74.8% of absorption enhancement at a regional scale in China. The source apportionment for EMAC offers a new diagnosis for each source regarding aerosol forcing simulation which inputs from the individual emission sector.

Emission Inventory and Characteristics of Anthropogenic Air Pollution Sources Based on Second Pollution Source Census Data in Sichuan Province

In this research, the activity data of Sichuan Province were collected using bottom-up and top-down methods. According to the second survey of pollution sources, the activity data of industrial source includes information of 11020 boilers and 60078 industrial enterprises. Data of 19152 industrial enterprises were collected in Chengdu, accounting for 32% of the total number of enterprises in Sichuan Province. The anthropogenic air pollutant emission inventory of 9 km×9 km was developed for Sichuan Province in 2017 with the use of appropriate emission estimation methods. The results showed that the total emission of SO2,NOx,CO,PM10,PM2.5,BC,OC,VOCs, and NH3 in Sichuan were 308.6×103, 725.7×103, 3131.2×103, 927.6×103, 422.4×103, 30.2×103, 72.0×103, 600.9×103, and 887.1×103 t. The fixed combustion source and process source mainly contributed as sources of SO2. The main source of CO was the process source and mobile source. Further, the dust source and process source were the main sources of PM10 and PM2.5, and the dust source was the largest source of BC and OC contributions. The emission sources of the VOCs were primarily the process sources, mobile sources and solvent use sources. The NH3 emissions were mainly from livestock and poultry breeding and nitrogen fertilizer applications. The spatial distribution results showed that the pollutants were mainly concentrated in the densely populated Sichuan basin and Panzhihua region, where industry and agriculture were relatively developed. The high value points are concentrated along the Deyang-Chengdu-Meishan-Leshan line in Chengdu Plain. The emission inventory established in this study still has certain uncertainties, and the accuracy of activity level data acquisition should be further enhanced. Moreover, pollutant emission factor testing should be carried out for typical pollution sources, and grid emission inventory should be improved to provide scientific support for the prevention and control of air pollution in Sichuan Province in the future.

Quantitative assessment of the variability in chemical profiles from source apportionment analysis of PM10 and PM2.5 at different sites within a large metropolitan area

The study aims to assess the differences between the chemical profiles of the major anthropogenic and natural PM sources in two areas with different levels of urbanization and traffic density within the same urban agglomeration. A traffic site and an urban background site in the Athens Metropolitan Area have been selected for this comparison. For both sites, eight sources were identified, with seven of them being common for the two sites (Mineral Dust, non-Exhaust Emissions, Exhaust Emissions, Heavy Oil Combustion, Sulfates & Organics, Sea Salt and Biomass Burning) and one, site-specific (Nitrates for the traffic site and Aged Sea Salt for the urban background site). The similarity between the source profiles was quantified using two statistical analysis tools, Pearson correlation (PC) and Standardized Identity Distance (SID). According to Pearson coefficients five out of the eight source profiles present high (PC > 0.8) correlation (Mineral Dust, Biomass Burning, Sea Salt, Sulfates and Heavy Oil Combustion), one presented moderate (0.8 > PC > 0.6) correlation (Exhaust) and two low/no (PC < 0.6) correlation (non-Exhaust, Nitrates/Aged Sea Salt). The source profiles that appear to be more correlated are those of sources that are not expected to have high spatial variability because there are either natural/secondary and thus have a regional character or are emitted outside the urban agglomeration and are transported to both sites. According to SID four out of the eight sources have high statistical correlation (SID < 1) in the two sites (Mineral Dust, Sea salt, Sulfates, Heavy Oil Combustion). Biomass Burning was found to be the source that yielded different results from the two methodologies. The careful examination of the source profile of that source revealed the reason for this discrepancy. SID takes all the species of the profile equally into account, while PC might be disproportionally affected by a few numbers of species with very high concentrations. It is suggested, based on the findings of this work, that the combined use of both tools can lead the users to a thorough evaluation of the similarity of source profiles. This work is, to the best of our knowledge, the first time a study is focused on the quantitative comparison of the source profiles for sites inside the same urban agglomeration using statistical indicators.

Chemical characteristics, oxidative potential, and sources of PM2.5 in wintertime in Lahore and Peshawar, Pakistan

The chemical characteristics, oxidative potential, and sources of PM2.5 were analyzed at the urban sites of Lahore and Peshawar, Pakistan in February 2019. Carbonaceous species, water soluble ions, and metal elements were measured to investigate the chemical composition and sources of PM2.5. The dithiothreitol (DTT) consumption rate was measured to evaluate the oxidative potential of PM2.5. Both cities showed a high exposure risk of PM2.5 regarding its oxidative potential (DTTv). Carbonaceous and some of the elemental species of PM2.5 correlated well with DTTv in both Lahore and Peshawar. Besides, the DTTv of PM2.5 in Lahore showed significant positive correlation with most of the measured water soluble ions, however, ions were DTT-inactive in Peshawar. Due to the higher proportions of carbonaceous species and metal elements, Peshawar showed higher mass-normalized DTT activity of PM2.5 compared to Lahore although the average PM2.5 concentration in Peshawar was lower. The high concentrations of toxic metals also posed serious non-carcinogenic and carcinogenic risks to the residents of both cities. Principle component analysis coupled with multiple linear regression was applied to investigate different source contributions to PM2.5 and its oxidative potential. Mixed sources of traffic and road dust resuspension and coal combustion, direct vehicle emission, and biomass burning and formation of secondary aerosol were identified as the major sources of PM2.5 in both cities. The findings of this study provide important data for evaluation of the potential health risks of PM2.5 and for formulation of efficient control strategies in major cities of Pakistan.

Trends in ambient air pollution levels and PM2.5 chemical compositions in four Chinese cities from 1995 to 2017.

An in-depth analysis of the specific evolution of air pollution in a given city can provide a better understanding of the chronic effects of air pollution on human health. In this study, we reported trends in ambient concentrations of particulate matter (PM) and gaseous pollutants [sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3)] from 1995 to 2017 and PM2.5 composition for the period of 2000–2017 in Guangzhou, Wuhan, Chongqing, and Lanzhou. We provided socio-economic indicators to help explain the pollution trends. SO2 and PM (including PM10 and PM2.5) concentrations showed a downward trend in recent years with the most notable reduction in SO2 in Chongqing and PM2.5 in Guangzhou. There was an overall flat trend for NO2, while O3 showed an upward trend in recent years except in Lanzhou. The majority of PM2.5 mass was SO42− (6.0–30 µg/m3) and organic carbon (6.0–38 µg/m3), followed by NO3− (2.0–12 µg/m3), elemental carbon (2.1–12 µg/m3), NH4+ (1.0–10 µg/m3), K+ (0.2–2.0 µg/m3), and Cl− (0.2–1.9 µg/m3). Except for secondary inorganic aerosols in Wuhan, annual average concentrations of all PM2.5 constituents showed a declining trend after 2013, corresponding to the trend of PM2.5. The secondary sources in PM2.5 were found to be most prominent in Wuhan, while the most abundant EC and Cl− in Lanzhou was attributed to the use of coal. Despite temporal and spatial variabilities across the four cities, coal combustion, traffic emissions, and secondary pollution have been the major sources of PM2.5 pollution. These trends in ambient air pollution levels and PM2.5 composition may help understand changes in health outcomes measured at different times within the time period of 1995–2017 in the four cities.

Neurodegenerative hospital admissions and long-term exposure to ambient fine particle air pollution

PurposeLong-term exposure to ambient fine particle (PM2.5) concentrations has been associated with an increased rate or risk of neurodegenerative conditions, but individual PM sources have not been previously examined in relation to neurodegenerative diseases. MethodsUsing the Statewide Planning and Research Cooperative System database, we studied 63,287 hospital admissions with a primary diagnosis of either Alzheimer's disease, dementia, or Parkinson's disease for New York State residents living within 15 miles from six PM2.5 monitoring sites. In addition to PM2.5 concentrations, we studied seven specific PM2.5 sources: secondary sulfate, secondary nitrate, biomass burning, diesel, spark-ignition emissions, pyrolyzed organic rich, and road dust. We estimated the rate of neurodegenerative hospital admissions associated with increased concentration of PM2.5 and individual PM2.5 sources average concentrations in the previous 0–29, 0–179, and 0–364 days. ResultsIncreases in ambient PM2.5 concentrations were not consistently associated with increased hospital admissions rates. Increased source-specific PM2.5 concentrations were associated with both increased (e.g., secondary sulfates and diesel emissions) and decreased rates (e.g., secondary nitrate and spark-ignition vehicular emissions) of neurodegenerative admissions. ConclusionsWe did not observe clear associations between overall ambient PM2.5 concentrations or source-apportioned ambient PM2.5 contributions and rates of neurologic disease hospitalizations.

Source apportionment of PM2.5 using online and offline measurements of chemical components in Tianjin, China

PM2.5 and chemical species of water-soluble inorganic ions, carbon components as well as elements were simultaneously measured by online and offline methods in Tianjin, China. Online measured PM2.5, total carbon, and major ions showed good agreement with offline results (r > 0.7), while obvious differences were found for most elements with correlation coefficients lower than 0.6. Online measured ions and most elements (except As, Ca, Ni) showed higher or comparable estimated annual average concentrations than offline results. Sources of PM2.5 were identified by using Positive Matrix Factorization (PMF) model respectively with online and offline data. Higher contributions of secondary source (38%) and secondary sulfate (11%) for online method could be partly caused by higher concentrations of SO42− and NO3− compared to offline method (30%, 10%). Lower source contribution of dust for online (4%) method might partly relate to lower concentration of Ca compared to offline measurement (7%). Different source contribution estimates of vehicle emission and coal combustion between online (18%, 24%) and offline (21%, 25%) may be partly linked to variation of concentrations of OC and EC resulted from different analytical methods. Industrial source, and sea salt & biomass burning showed lower source contributions both online (4%, 1%) and offline (5%, 2%).

Interval estimation in multivariate curve resolution by exploiting the principles of error propagation in linear least squares

Interval estimation in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) is a challenging problem. Several algorithms including Bayesian, Monte-Carlo, bootstrap and jackknife resampling approaches have been proposed previously to address this problem in MCR. In the present contribution, constructing the confidence intervals (CIs) in MCR-ALS resolved profiles using the principles of error propagation in linear least squares (LS) parameter estimation is proposed. In MCR-ALS, every set of profiles in the row subspace (scores) can be considered as coefficients for estimating their counterparts in the columns subspace (loadings) and vice versa. Therefore, it should be possible to calculate the CIs for these coefficients (scores or loadings) using the principles of error propagation in LS. The confidence intervals of the coefficients in linear regression are directly related to the inverse of the matrix of sums of squares and sums of cross products of independent variables and to the standard deviation of the residuals. This idea has been successfully applied in this work for the construction of the CIs of the resolved profiles in the MCR-ALS algorithm. The proposed approach is named ‘Confidence Intervals based on Least Squares’ (CILS). The weighted version of this approach has also been implemented and named as CIWLS. The latter method can be used for handling datasets with a known type of error structure. The performances of the CILS and CIWLS approaches are evaluated in this work for the calculation of the CIs for several simulated three component LC-MS and LC-DAD datasets, with different homo- and heteroscedastic noise levels. The CIs obtained by CILS and CIWLS are compared with those obtained by the Monte-Carlo noise addition method. The empirical coverage probabilities of the CIs are also computed to check if the calculated CIs achieve the nominal confidence level. The average standard errors (ASE) are used as measures of the level of uncertainty (precision) in the recovered profiles. The results obtained in this work revealed that the CIs obtained by the CILS method are in agreement with those obtained by the Monte-Carlo approach. The main advantage of the CILS method is that the calculations are faster and require less computation time. Finally, the performance of the CIWLS algorithm was assessed in the analysis of a real environmental dataset for the source apportionment of particulate matter (PM) in air samples collected in Northern Spain. The results obtained by the CIWLS method were in agreement with those previously reported for this real dataset and the CIs of the contribution and composition profiles of the PM10 sources were properly estimated.

Systematic bias of WRF-CMAQ PM10 simulations for Seoul, Korea

For evaluating the performance of the Korean operational air quality model (Weather Research and Forecasting-Community Multiscale Air Quality), we compared the simulated concentrations of particulate matter with diameters ≤ 10 μm (PM10) against observations in Seoul, Korea for the cold seasons (October through March) of 2015–2018. The simulations yield about 70% hit rate for PM10 concentrations within 10 μg m−3 from the observations. For the 50–90 μg m−3 range, the simulations systematically overestimate the PM10 frequency mainly due to overestimation/underestimation of PM10 concentrations in the moderate-PM10 (30 < PM10 ≤ 60 μg m−3)/high-PM10 (60 < PM10 ≤ 100 μg m−3) range. Considering that the model simulates PM10 transports using input meteorological fields and emissions amounts, 72-h back-trajectories from Seoul (37.51°N, 126.99°E) at the 500 m above ground level are analyzed to investigate the cause of the systematic bias. The 410 days when PM10 concentrations are within the 30–100 μg m−3 range are grouped into three major wind flows: westerly (163 days), northerly (99 days), and northwesterly (148 days). Overestimations of moderate-PM10 days frequently occur for the westerly group associated with a synoptic pattern characterized by calm anticyclonic systems over China and Korea, especially when the emission amounts were overestimated in eastern China, the main PM10 source region for Seoul for the westerly group. Underestimations of high-PM10 days frequently occur for the northerly and northwesterly groups associated with climatological patterns or strong windy cyclonic systems along with some small inflow from underestimated emission in northeastern China. To improve the model performance, statistical models trained for each back-trajectory group along with improved estimations of emission amounts may prove helpful.

Inhalation bioaccessibility estimation of polycyclic aromatic hydrocarbons from atmospheric particulate matter (PM10): Influence of PM10 composition and health risk assessment

Polycyclic aromatic hydrocarbons (PAHs) inhalation bioaccessibility was assessed in 65 atmospheric particulate matter samples (PM10) collected at an Atlantic coastal European urban site. The proposed method consists on a physiologically based extraction (PBET) by using Gamble’s solution followed by a vortex assisted liquid-liquid micro-extraction (VALLME) and quantification by high performance liquid chromatography with fluorescence detection (HPLC-FLD). The use of a micro-extraction technique combined with FLD detection, provides a simple, fast, sensitive, accurate and low-cost methodology to PAHs quantification in bioaccessible fractions. Accuracy of the bioaccessibility study was assessed by means of a mass balance approaches using a PM10 filter and a certified reference material (ERM-CZ100). High-moderate inhalation bioaccessibilities were found for phenanthrene (Phe), fluoranthene (Ft) and pyrene (Pyr) (average ratios in the 52–65% range); while dibenz (a,h)anthracene (DBahA), indeno (1,2,3-cd)pyrene (IP) and benzo (g,h,i)perylene (BghiP) were observed to be less bioaccessibles (average ratios in the 11–14% range). Relationship between PM10 composition (major ions, trace metals, equivalent black carbon (eBC) and UV-absorbing particulate matter (UVPM)) and PAHs bioaccessibility ratios was also assessed. Principal Component Analysis (PCA) showed that PAHs bioaccessibility percentage is dependent on anthropogenic (eBC, UVPM and Sb concentrations) and marine sources of PM10. Predicted PAHs bioaccessibilities after applying a multiple linear regression model based on marine and anthropogenic source of PM10 could also be established. Health risk assessment of target PM10-associated PAHs via inhalation was assessed considering bioaccessibility concentrations by using hazard index (HI) and BaP equivalent concentration (BaPeq) approaches, suggesting no carcinogenic risk in the area during the sampling campaign.

Distribution characteristics and policy-related improvements of PM2.5 and its components in six Chinese cities

This study presents the distribution characteristics and possible sources of fine particulate matter (PM2.5) and its components, as well as policy-related pollution reduction in the Chinese cities of Jinan, Shijiazhuang (SJZ), Chengdu, Wuxi, Wuhan, and Harbin (HRB). PM2.5 samples were collected using mid-volume samplers during the autumn of 2017 in all six cities. The samples were analyzed to determine the ambient PM2.5 compositions, including the concentrations of water-soluble inorganic ions (WSIIs), carbonaceous aerosols, and elements concentrations. The chemical ratios of organic carbon to elemental carbon and nitrate to sulfate as well as the enrichment factors of elements were calculated to establish the possible sources of PM2.5 in all six cities. The highest PM2.5 concentration was 152 μg/m3 in SJZ, while the lowest concentration was 47 μg/m3 in HRB. During the sampling period in these six cities, the PM2.5 concentrations exceeded the World Health Organization recommended daily average air quality guidelines by 2.4–6.1 times, and WSIIs, carbonaceous aerosols, and elements accounted for 31.8%–61.6%, 9.8%–35.1%, and 0.9%–2.5% of the PM2.5, respectively. In 2013, the Chinese government formulated the Air Pollution Prevention and Control Action Plan (APPCAP) for controlling air pollution, and effective measures have been implemented since then. Compared with previous studies conducted during 2009–2013 before the implementation of the APPCAP, the concentrations of PM2.5 and most of its components decreased to varying degrees, and large changes in the chemical ratios of PM2.5 components were observed. These results indicate that PM2.5 sources vary among these six cities and that China has improved the ambient air quality in these cities through the implementation of air pollution control policies. The APPCAP have achieved considerable results in continuously reducing pollution concentrations, although the air pollution concentrations observed in this study remain high compared with those of other countries.

Method for High-resolution Emission Inventory for Road Vehicles in Chengdu Based on Traffic Flow Monitoring Data

A method for developing a high-resolution emission inventory for road vehicles based on traffic flow monitoring data is proposed in this study. The characteristics of road traffic flow were analyzed and a high-resolution emission inventory of vehicle in Chengdu was established. The results showed that the traffic flow and emissions in Chengdu exhibited an obvious "double peak" distribution, and that the traffic volume of vehicles during peak hours accounted for 39.85% of the total. China IV vehicles, small vehicles, and gasoline vehicles were the main types of road vehicles classified. The daily emissions of SO2, NOx, CO, PM10, PM2.5, BC, OC, and VOCs from road vehicles were 3.89, 162.08, 324.11, 4.79, 4.36, 1.89, 0.78, and 44.37 t, respectively. The overall spatial distribution showed a decreasing trend from the city center to the periphery, and the time distribution essentially presented a "double peak" distribution. The related indicators of particulate matter were greatly affected by the number of trucks. The main source of NOx, PM10, PM2.5, BC, and OC was large diesel vehicles, and the main source of CO was small gasoline vehicles. NOx emissions from large vehicles accounted for up to 80% of the total. The method based on registered vehicles led to an overestimation of the emissions from road vehicles in Chengdu, with a proportion between 1% and 30%.

Beyond Strict Regulations to Achieve Environmental and Economic Health-An Optimal PM2.5 Mitigation Policy for Korea.

Growing concern about particulate matter (PM2.5) pressures Korea to reduce the health risks associated with its high dependency on fossil fuels. The Korean economy relies heavily on large thermal power plants—a major source of PM2.5 emissions. Although air quality regulations can negatively impact local economies, the Korean government announced two strict air quality mitigation policies in 2019. We develop a regional static computable general equilibrium model to simulate the economic and environmental impacts of these polices under alternative hypothetical scenarios. We separate two regions, Chungcheongnam-do, the most polluted region, and the rest of the country, in our model. As policy options, we introduce a regional development tax and a tradable market for PM emission permits, similar to an air pollution tax and a carbon permits market, respectively. The results show that allowing higher tax rates and a tradable permits market gives the optimal combination, with the PM2.5 emissions reduced by 2.35% without sacrificing economic growth. Since alternative options present, for example, a 0.04% loss of gross domestic product to reduce PM emissions by the same amount, our results here may present a new policy paradigm for managing air pollutants such as PM2.5.