Decrease In PM2 Research Articles

A Study on Ambient Air Quality and Management inCommercial Areas of Gorakhpur City

The current study evaluates the city of Gorakhpur's ambient air quality using an air quality index. For the period January 2018 to November 2022, the 24-hour average concentration of the three primary pollutants SO2, NO2, andPM10 in a commercial area close to Jalkal Bhawan in Gorakhpur city was measured. Pollutant measurements were made between January 2018 and November 2022. The IND-AQI technique was implemented to calculate the Air Quality Index. In July, the highest monthly average concentrations of PM10, SO2, and NO2 were recorded at 401.13, 36.41, and 54.02, respectively. The yearly average SO2 concentration was found to be within the CPCB- recommended limit, while NO2 was found to be slightly above the maximum allowable level. However, PM10 wasconsistently found to be above the CPCB-recommended limit. The average monthly concentration of NO2 is higherthan SO2, and the overall AQI rating was determined to be moderate. The global experiences clearly demonstratedthat international action had been city specific rather than country oriented, and as a result, statistics show a reductionin PM2.5 of 35%–40% in five years for cities like Beijing and Seoul, whereas cities like Santiago and Mexico Cityhave shown reductions in PM2.5 and PM10 concentration of 73% and 61% in 20 to 25 years, respectively. Recent research published in the Lancet found that over the past five years, CO and Sulphur dioxide levels decreased by 28.2% and 54.1%, respectively, while NO2 and O3 concertation showed no considerable improvement. The August2018 TERI and ARAI report examined several interventions and predicted their possible effects. PM2.5 and PM10 levelsin Delhi and the NCR are above average. A different scenario has been developed in light of the intervention that can improve air quality the most. The NCAP produces an estimated national level target of 20%-30% decreasesin PM2.5 and PM10 concentration by 2024 after taking into consideration the existing international and national studies. This keeps 2018 as the baseline year for concentration comparisons. Gorakhpur City has recently experienced an increase in development activity, which has led to particulate matter.

Spatiotemporal Characterization and Driving Factors of Fine Particulate Matter and Its Chemical Components in the Huaihe River Basin

According to the data sets of fine particulate matter （PM2.5） and its components in 35 cities in the Huaihe River Basin from 2015 to 2021, the temporal and spatial distribution patterns of pollutants were analyzed. The influence of meteorological factors on PM2.5 concentrations was examined using a random forest model. The original series of PM2.5, sulfate （SO42-）, nitrate （NO3-）, ammonium salt （NH4+）, organic matter （OM）, and black carbon （BC） were rebuilt using KZ （Kolmogorov-Zurbenko） filtering and multiple linear regression （MLR） to quantify the effects of meteorological conditions. The results demonstrated that from 2015 to 2021, the declining rates of PM2.5, SO42-, NO3-, NH4+, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m3·a）-1, respectively. The high mass concentrations of PM2.5 and its components were concentrated in the central and western regions of the HRB, whereas those in coastal and southern cities were lower. The variance contributions of the short-term, seasonal, and long-term components of PM2.5 to the original PM2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM2.5, SO42-, NO3-, NH4+, OM, and BC were 28.3%, 29.1%, 31.0%, 29.3%, 27.8%, and 28.6%, respectively. The contribution rates of meteorological conditions to the long-term PM2.5 reduction were 43.4%, 25.6%, 25.5%, and 20.6% in the HRB cities in Anhui, Shandong, Jiangsu, and Henan Provinces, respectively. With the decrease in PM2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little.

Effects of Wildfire Smoke on Volatile Organic Compound (VOC) and PM2.5 Composition in a United States Intermountain Western Valley and Estimation of Human Health Risk

With a warmer and drier climate, there has been an increase in wildfire events in the Northwest US, posing a potential health risk to downwind communities. The Lewis–Clark Valley (LCV), a small metropolitan area on the Washington/Idaho border in the United States Intermountain West region, was studied over the time period of 2017–2018. The main objective was to determine the community’s exposure to particulate matter (PM2.5) and volatile organic compounds (VOCs) during wildfire smoke events and to estimate the associated health risk. VOCs were analyzed previously in the LCV using sorbent tube sampling and thermal-desorption gas-chromatography mass-spectrometry (TD-GC-MS) during several local smoke events in the 2017–2018 fire seasons. PM2.5 measurements were obtained from nearby agency monitors. PM2.5 reached up to 200 µg/m3 in 2017 and over 100 µg/m3 in 2018 in the LCV, and has been observed to be increasing at a rate of 0.10 µg m−3/yr over the past two decades. Benzene, a carcinogen and air toxic, was measured with concentrations up to 11 µg/m3, over ten times the normal level in some instances, in the LCV. The health risk in the LCV from benzene was calculated at seven extra cancers per million for lifetime exposure and thirteen extra cancers per million considering all air toxics measured. The other cities monitored showed similar lifetime cancer risk, due to benzene of about 6–7 extra cancers per million. This work is important, as it measures ground-level exposures of VOCs and demonstrates decreases in PM2.5 air quality over time in the region.

The environmental consequences of industrial agglomeration: New evidence from city-level data in China

In the context of sustainable development, environmental policies increasingly focus on reducing carbon emissions worldwide. Industrial agglomeration (IAGG) has emerged as a potential factor in achieving this objective by reducing carbon emissions and improving air quality. This study examines the direct and indirect impacts, spatial dynamics, and policy impacts of industrial agglomeration on air pollution levels. Panel data from 278 cities in China between 2011 and 2020 are utilized to investigate the potential of industrial agglomeration to reduce air pollution. Multiple regression models are employed, these models explore the mechanisms through which industrial agglomeration can have a positive effect on air pollution, highlighting economic growth, inclusive finance, digital economy and technological innovation as crucial factors. The study also investigates the spatial spillover effects of industrial agglomeration, finding that the concentration ratio and specialization of neighbouring cities significantly contribute to local air pollution levels. Furthermore, this contribution increases progressively with distance. The study investigates the spillover effects of environmental pollution and industrial agglomeration-related policies on PM2.5 concentration, concluding that such spillover effects lead to a decrease in PM2.5 concentration in neighbouring locations.

Synthesis of satellite and ground data provide unique perspectives for discovering the air pollution patterns: A case study in Guangdong Province, China

Existing studies have analyzed the spatio-temporal patterns of air pollutants by combining ground and satellite measurements, primarily for cross-validation purposes. However, the unique characteristics and discrepancies between satellite and ground measurements have rarely been leveraged to understand pollution patterns and identify air pollution sources. To our best knowledge, this study is the first to utilize these discrepancies to holistically analyze the spatial and temporal patterns and investigate local biomass-burning effects on the five typical air pollutants: particulate matter (PM2.5)/aerosol optical depth (AOD), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and ozone (O3). Guangdong (GD) province was selected as a case study due to its complex air pollution sources and patterns. Ground-based analysis from 2015 to 2023 shows significant decreases in PM2.5, CO, NO2, and SO2, and a significant increase in O3 in urban areas, indicating the efficacy of stringent air pollution control policies. However, satellite analysis shows significant downtrend only in AOD, while the trends of other pollutants are almost negligible, which are likely to be evidence of industrial migration. Both measurements exhibit regular seasonal patterns for all air pollutants. In-depth time-series comparisons between ground and satellite data reveal seasonal consistency for NO2 but noticeable discrepancies for both AOD and CO, which could be attributed to urban-rural differences and local versus transported pollution sources. Spatially, AOD and NO2 exhibits the most significant regional discrepancies, followed by SO2 and CO, with higher values observed over Pearl River Delta (PRD) compared to non-PRD regions. O3 is more evenly distributed, showing more pronounced seasonal variations than regional differences. The synergetic use of satellite and ground measurements collectively verifies the significant local biomass-burning effects on the five pollutants. These findings can aid in developing more targeted air pollution control policies.

An annual result of outdoor and indoor PM 2.5 analysis in two different building types in Ulaanbaatar, Mongolia.

Air pollution has been a significant environmental and public health concern in Ulaanbaatar, the capital city of Mongolia, for many years. The city experiences severe air pollution, particularly during the winter months. This study investigated the annual trends of outdoor and indoor PM2.5 concentrations at two neighboring sites in Ulaanbaatar: an office and a household, using low-cost sensors. Both locations exhibited similar fluctuations in outdoor PM2.5 concentrations over time, with ambient PM2.5 levels beginning to rise in October and declining in April. During the mid-term of the heating season (November to February), hourly averaged PM2.5 concentrations were exceptionally high, with peak pollution events exceeding ~ 1000µg/m3. Notably, PM2.5 concentrations were elevated during this heating season period. Time-activity patterns showed a decrease in PM2.5 concentrations during the periods of 06:00-08:00 and 14:00-18:00. Furthermore, the study found that the indoor environment could remain safe when windows were well-sealed, even under severe outdoor pollution conditions. Overall, this study provided accurate insights into the annual patterns of PM2.5 concentrations and demonstrated their fluctuations during the heating season when pollution levels were particularly high. The findings offer valuable recommendations for individuals to consider when going outside and for taking actions to improve indoor air quality in Ulaanbaatar.

P031 SPATIAL ASSOCIATION BETWEEN HYPERTENSION AND AIR POLLUTION IN ULAANBAATAR, MONGOLIA

Background and Objective: This study aims to assess the spatial association between hypertension prevalence and PM2.5 levels in Ulaanbaatar. Methods: To estimate the hypertension prevalence, we conducted a complex survey with a two-stage cluster sampling method in 2018. For PM2.5 level estimation, we used data collected during cold weather months (October to March) from 2015 to 2019. These estimates were used to predict PM2.5 levels across the entire city, using the Inverse Distance Weighted (IDW) interpolation method. The same approach was applied to predict the hypertension prevalence. We fitted a multiple linear regression model to estimate the association between PM2.5 and hypertension prevalence with adjustment for income, a proxy of socioeconomic factors to examine effect modification. In exploratory analysis, we estimated the association between PM2.5 level and income level. We used RStudio 1.3.1093 and ArcMap 10.8.2 for data analysis. Results: A total of 4515 participants were included in the sample. The hypertension prevalence was 46.5% with a threshold of 130/80 mm Hg. Adjusting for other covariates, a 10-unit increase in PM2.5 level is associated with a 6.66 (95% CIs;[10.23,10.23]) point increase in hypertension prevalence (p<0.001). With each 100,000 (MNT) increase in income level, the impact of 10-unit increases in PM2.5 level on hypertension decreases by 2.98 (95% CIs;[-5.48,-0.47]) points (p=0.02). In the partial F-test, an increase in income level was associated with a lower hypertension prevalence (p=0.002). In the exploratory analysis, a 100,000 (MNT) increase in income level was associated with a 0.23 (95% CIs;[-0.41,-0.05]) point decrease in PM2.5 level (p=0.011). Figure 1. Spatial association between hypertension prevalence. A).Hypertension prevalence (%); B).Income level (MNT) PM2.5 level; C).PM 2.5 (μg/m3); D).Linear regression model. Note: Inverse Distance Weighted method is used for predictions. Green points indicate the PM2.5-measuring site locations. Conclusions: The people who live in lower-income areas experience a stronger influence of air pollution on hypertension prevalence compared to others in Ulaanbaatar.

Warming of mid-latitude North Atlantic Sea surface temperature strengthens the daily variability of winter PM2.5 in eastern China

Abstract Climate factors, in addition to human activities, are acknowledged to exert a notable influence on the synoptic PM2.5 variations over eastern China in extensive case studies. Based on observed daily PM2.5 concentrations data, this study reveals the enhanced daily variability (DV) of PM2.5 concentrations over eastern China and identifies its association with mid-latitude sea surface temperature anomalies over the North Atlantic. The dominant daily mode of PM2.5 concentrations identified through empirical orthogonal function analysis accounts for 43.75% of the total variance, with its DV experienced a significant enhancement from 1979 to 2019. The identified enhancement is attributed to the intensified wave train propagation along the mid-latitudes on a time scale of 10–30 d. The eastward propagation of the identified wave train can expose eastern China to recurrent influences of cyclonic and anticyclonic anomalies, resulting in an initial increase and subsequent decrease in PM2.5 concentrations. Statistical analysis and dynamic diagnostics show that the warming of the mid-latitude North Atlantic Ocean enhances the wave train at its source through the local energy exchange, and ultimately leads to an increased DV of PM2.5 concentrations.

Decreasing trends in PM2.5 and BC concentrations observed on central and southwestern Japanese Islands

The interannual trends in fine particulate matter (PM2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM2.5 and BC were observed during the spring season (April) in both locations. The PM2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM2.5. The total reductions in PM2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m−3, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m−3year−1, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m−3, amounting to 0.018 ± 0.014 μg m−3year−1 reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, >40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.

Spatiotemporal Trends and Driving Factors of PM2.5-related Health Burden in Gansu Province, China from 2013 to 2020

To evaluate the spatiotemporal trends and drivers of PM2.5-related health effects in Gansu Province since the implementation of the Air Pollution Prevention and Control Action Plan, the latest global exposure mortality model （GEMM） was adopted to estimate the health burden attributable to PM2.5 in Gansu Province from 2013 to 2020. The factor decomposition method was used to further quantify the main causes of the long-term changes in deaths attributable to PM2.5 pollution. The results showed that from 2013 to 2020, the population-weighted PM2.5 concentration in Gansu Province decreased by 34.57%, and the proportion of people exposed to areas with an annual average PM2.5 concentration exceeding 35 μg·m-3 decreased significantly from 72.89% to 11.61%. Moreover, the number of attributable deaths in Gansu Province declined from 12 826 （95%CI： 7 840-17 408） in 2 013 to 9 814 （95%CI： 6 407-13 036） in 2020, indicating a decrease of 23.48%. Attributable deaths from stroke, chronic obstructive pulmonary disease, lung cancer, and lower respiratory infection declined, whereas deaths from ischemic heart disease increased by 12.11%. Notably, individuals aged 60 years and older accounted for more than 80% of all age-related deaths. The number of deaths attributable to PM2.5 in central and eastern Gansu Province was significantly higher than that in the Hexi region, and most regions showed a downward trend. The contribution of the total population, age structure, baseline mortality rate, and PM2.5 concentration to the change in PM2.5-related deaths was -1.26%, 16.16%, -9.84%, and -28.55%, respectively. Overall, population aging and a decrease in PM2.5 concentration were the main factors contributing to the increase and decrease in PM2.5-related deaths, respectively. The active clean air policies in Gansu Province have reduced the health burden caused by PM2.5 pollution, but with the trend of population aging, a significant reduction in PM2.5 concentration will be needed in the future to avoid more attributable deaths.

Improved Air Quality and Asthma Incidence from School Age to Young Adulthood: A Population-based Prospective Cohort Study.

Rationale: The benefits of improved air quality on asthma remain understudied. Objectives: Our aim was to investigate associations of changes in ambient air pollution with incident asthma from school age until young adulthood in an area with mostly low air pollution levels. Methods: Participants in the BAMSE (Swedish abbreviation for Children, Allergy, Environment, Stockholm, Epidemiology) birth cohort from Stockholm without asthma before the 8-year follow-up were included (N = 2,371). We estimated the association of change in individual-level air pollutant exposure (particulate matter with an aerodynamic diameter ≤ 2.5 μm [PM2.5] and ≤ 10 μm [PM10], black carbon [BC], and nitrogen oxides [NOx]) from the first year of life to the 8-year follow-up with asthma incidence from the 8-year until the 24-year follow-up. Multipollutant trajectories were identified using the group-based multivariate trajectory model. We also used parametric G-computation to quantify the asthma incidence under different hypothetical interventions regarding air pollution levels. Results: Air pollution levels at residency decreased during the period, with median reductions of 5.6% for PM2.5, 3.1% for PM10, 5.9% for BC, and 26.8% for NOx. A total of 395 incident asthma cases were identified from the 8-year until the 24-year follow-up. The odds ratio for asthma was 0.89 (95% confidence interval [CI], 0.80-0.99) for each interquartile range reduction in PM2.5 (equal to 8.1% reduction). Associations appeared less clear for PM10, BC, and NOx. Five multipollutant trajectories were identified; the largest reduction trajectory displayed the lowest odds of asthma (odds ratio, 0.55; 95% CI, 0.31-0.98) compared with the lowest reduction trajectory. If the PM2.5 exposure had not declined up to the 8-year follow-up, the hypothetical asthma incidence was estimated to have been 10.9% higher (95% CI, 0.8-20.8%). Conclusions: A decrease in PM2.5 levels during childhood was associated with a lower risk of incident asthma from school age to young adulthood in an area with relatively low air pollution levels, suggesting broad respiratory health benefits from improved air quality.

Varying Drivers of 2013–2017 Trends in PM2.5 Pollution over Different Regions in China

A significant decrease in surface PM2.5 concentrations has been reported since the implementation of the Air Pollution Prevention and Control Action Plan in 2013. In this study, we use the GEOS-Chem model to simulate the trend in surface PM2.5 pollution in China from 2013 to 2017, as well as the relative contributions of emission reduction and meteorology. The simulated decline rate averaged over monitoring sites in China is around −4.7 μg m−3 yr−1 in comparison with the value of −6.4 μg m−3 yr−1 from observations. The model also captures the variations over different regions, with r in the range of 0.85–0.95. Based on the sensitivity tests against emissions and meteorology, the study finds that the decline in PM2.5 concentrations is mainly driven by the reduction in anthropogenic emissions. The variation in open biomass burning (OBB) is not significant, except in Northeast China (NEC) and Pearl River Delta (PRD), where the changes originated from OBB are 40% and 30% of those associated with anthropogenic emission reductions. Changes in meteorology from 2013 to 2017 led to significant increases in PM2.5 concentrations in most areas in China, except in NEC. The increase attributed to meteorology, to a large extent, could be explained by the significant decrease in surface wind speed (WS) and planetary boundary layer height (PBLH) between 2013 and 2017, combined with their negative correlation with PM2.5. The decrease in PM2.5 concentrations in NEC, on the other hand, could be explained by the significant decrease in relative humidity (RH) there combined with the positive correlation of RH with PM2.5, while the changes in WS and PBLH there are relatively small compared with other areas. The change in meteorology, therefore, hinders the improvement of air quality via emission controls in most of China. In Sichuan Basin (SCB), the increase due to meteorology almost compensates for the decrease associated with emission reduction, leading to the least change in PM2.5 concentrations, although the decrease due to emission controls is the largest compared with other areas.

Time series analysis of PM2.5 pollution risk based on the supply and demand of PM2.5 removal service: a case study of the urban areas of Beijing.

Demonstrating the temporal changes in PM2.5 pollution risk in regions facing serious PM2.5 pollution problems can provide scientific evidence for the air pollution control of the region. However, research on the variation of PM2.5 pollution risk on a fine temporal scale is very limited. Therefore, we developed a method for quantitative characterizing PM2.5 pollution risk based on the supply and demand of PM2.5 removal services, analyzed the time series characteristics of PM2.5 pollution risk, and explored the reasons for the temporal changes using the urban areas of Beijing as the case study area. The results show that the PM2.5 pollution risk in the urban areas of Beijing was close between 2008 and 2012, decreased by approximately 16.3% in 2016 compared to 2012, and further decreased by approximately 13.2% in 2021 compared to 2016. The temporal variation pattern of the PM2.5 pollution risk in 2016 and 2021 showed significant differences, including an increase in the number of risk-free days, a decrease in the number of heavily polluted days, and an increase in the stability of the risk day sequence. The significant reduction in risk level was mainly attributed to Beijing's air pollution control measures, supplemented by the impact of COVID-19 control measures in 2021. The results of PM2.5 pollution risk decomposition indicate that compared to the previous 2years, the stability and predictability of the risk variation in 2016 increased, but the overall characteristics of high risk from November to February and low risk from April to September did not change. The high risk from November to February was mainly due to the demand for coal heating during this period, a decrease in PM2.5 removal service supply caused by plant leaf fall, and the common occurrence of temperature inversions in winter, which hinders the diffusion of air pollutants. This study provides a method for the analysis of PM2.5 pollution risk on fine temporal scales and may provide a reference for the PM2.5 pollution control in the urban areas of Beijing.

Synergistic Evolution of PM2.5 and O3 Concentrations: Evidence from Environmental Kuznets Curve Tests in the Yellow River Basin

Air pollution, especially the synergistic pollution of PM2.5 and O3, poses a severe threat to human life and production. The synergistic formation mechanism of PM2.5 and O3 pollution is relatively confirmed, while research on their spatiotemporal synergy is urgent. Based on remotely sensed interpretation data from 90 cities in the Yellow River Basin, we analyzed the synergistic evolution of PM2.5 and O3 concentrations during 2013–2020. Combined with the environmental Kuznets curve, we performed factor analysis using a panel regression model. The synergistic evolution pattern shows a gradual decrease in PM2.5 concentration and a gradual increase in O3 concentration. There is a strong spatial auto-correlation in the two pollutants’ concentrations. The relationship between economy and PM2.5 concentration shows an “N-shaped” curve, while that between O3 concentration and economic development presents an inverse “N-shaped” curve. The environmental Kuznets curve shows that the deterioration of O3 pollution takes place later than the mitigation of PM2.5 pollution. Various factors have obvious heterogeneous effects on PM2.5 and O3 concentrations. Meanwhile, the sensitivity effect of per capita GDP on PM2.5 concentration in the midstream region is stronger than that in the upstream region, while the sensitivity effect of per capita GDP on O3 concentration is strongest in the midstream region than that in upstream and downstream region.

Changes of PM2.5 and O3 and their impact on human health in the Guangdong-Hong Kong-Macao Greater Bay Area.

In recent years, the combined pollution of PM2.5 and O3 in China, particularly in economically developed regions such as the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), has garnered significant attention due to its potential implications. This study systematically investigated the changes of PM2.5 and O3 and their associated human health effects in the GBA, utilizing observational data spanning from 2015 to 2019. The findings revealed a spatial trend indicating a gradual decrease in PM2.5 levels from the northwest to the southeast, while the spatial distribution of MDA8 O3 demonstrated an opposing pattern to that of PM2.5. The monthly fluctuations of PM2.5 and MDA8 O3 exhibited V-shaped and M-shaped patterns, respectively. Higher MDA8 O3 concentrations were observed in autumn, followed by summer and spring. Over the five-year period, PM2.5 concentrations exhibited a general decline, with an annual reduction rate of 1.7μgm-3/year, while MDA8 O3 concentrations displayed an annual increase of 3.2μgm-3. Among the GBA regions, Macao, Foshan, Guangzhou, and Jiangmen demonstrated notable decreases in PM2.5, whereas Jiangmen, Zhongshan, and Guangzhou experienced substantial increases in MDA8 O3 levels. Long-term exposure to PM2.5 in 2019 was associated with 21,113 (95% CI 4968-31,048) all-cause deaths (AD), 1333 (95% CI 762-1714) cardiovascular deaths (CD), and 1424 (95% CI 0-2848) respiratory deaths (RD), respectively, reflecting declines of 27.6%, 28.0%, and 28.4%, respectively, compared to 2015. Conversely, in 2019, estimated AD, CD, and RD attributable to O3 were 16,286 (95% CI 8143-32,572), 7321 (95% CI 2440-14,155), and 6314 (95% CI 0-13,576), respectively, representing increases of 45.9%, 46.2%, and 44.2% over 2015, respectively. Taken together, these findings underscored a shifting focus in air pollution control in the GBA, emphasizing the imperative for coordinated control strategies targeting both PM2.5 and O3.

Can ecological compensation reduce air pollution? New evidence from resource-based cities in China

ABSTRACT China has made great efforts to establish an ecological compensation mechanism, but there lacks empirical evidence on whether this scheme effectively reduces air pollution. To test the effectiveness of air quality ecological compensation (AQEC) on air pollution control, this study considers 114 resource-based cities in China and uses a multi-period difference-in-difference (DID) model for empirical analysis. The finding shows that the AQEC policy significantly reduces the concentration of air pollutants by promoting air pollution prevention and local authority enthusiasm for pollution abatement, resulting in an average annual decrease in PM2.5 concentrations of approximately 3.9 µg/m3 in the pilot cities. The AQEC policy of resource-based cities in eastern and northern China, and those with less financial pressure have greater inhibitory effects on air pollution. The study recommends establishing long-term protection mechanisms and implementing differentiated policies focused on green technological innovation and financial autonomy.

Chemical characterization and source apportionment of PM2.5 in a Northeastern China city during the epidemic period.

To investigate the influence of COVID-19 lockdown measures on PM2.5 and its chemical components in Shenyang, PM2.5 samples were continuously collected from January 1 to May 31, 2020. The samples were then analyzed for water-soluble inorganic ions, metal elements, organic carbon, and elemental carbon. The findings indicated a significant decrease in PM2.5 and its various chemical components during the lockdown period, compared to pre-lockdown levels (p < 0.05), suggesting a substantial improvement in air quality. Water-soluble inorganic ions (WSIIs) were identified as the primary contributors to PM2.5, accounting for 47% before the lockdown, 46% during the lockdown, and 37% after the lockdown. Ionic balance analysis revealed that PM2.5 exhibited neutral, weakly alkaline, and alkaline characteristics before, during, and after the lockdown, respectively. NH4+ was identified as the main balancing cation and was predominantly present in the form of NH4NO3 in the absence of complete neutralization of SO42- and NO3-. Moreover, the higher sulfur oxidation ratio (SOR) and nitrogen oxidation ratio (NOR), along with the significant increase in PM2.5/EC, suggested intense secondary transformation during the lockdown period. The elevated OC/EC ratio during the lockdown period implied higher secondary organic carbon (SOC), and the notable increase in SOC/EC ratio indicated a significant secondary transformation of total carbon. The enrichment factor (EF) results revealed that during the lockdown, 9 metal elements (As, Sn, Pb, Zn, Cu, Sb, Ag, Cd, and Se) were substantially impacted by anthropogenic emissions. Source analysis of PMF was employed to identify the sources of PM2.5 in Shenyang during the study period, and the analysis identified six factors: secondary sulfate and vehicle emissions, catering fume sources, secondary nitrate and coal combustion emissions, dust sources, biomass combustion, and industrial emissions, with secondary sulfate and vehicle emissionsand catering fume sourcescontributing the most to PM2.5.

Response of warm season secondary pollutants to emissions and meteorology in the North China Plain region during 2018–2022

Air quality in China has continued to improve since 2013, although severe pollution events still occur over the North China Plain (NCP). It is noticeable that contributions from secondary pollutants have increased, but understanding of their formation and variations with changing emissions and meteorological conditions remains limited. In this study, the warm season of May to September from 2018 to 2022 was selected to explore the response of secondary pollutants to meteorology and emissions using the Community Multi-scale Air Quality model (CMAQ). Fine particulate matter (PM2.5) concentrations over the NCP decreased significantly by 31%–37% from 2018 to 2022, while ozone (O3) and nitrogen dioxide (NO2) generally showed decreasing trends by 1% and 0.5% per year, respectively. Secondary inorganic aerosol (including sulfate, nitrate, and ammonium) and secondary organic aerosol (SOA) also decreased significantly, by 9% and 6% per year, respectively. The results showed that emissions contributed 96% to the decreases in PM2.5 concentrations, while O3 fluctuated due to meteorological changes. Although the decreases in sulfate and ammonium were mainly associated with emission reductions, that of nitrate was more sensitive to meteorological changes. Meteorological and emission changes were similarly important for the overall decrease in SOA, with anthropogenic SOA being more sensitive to emissions control, while biogenic SOA was more easily attributed to meteorological changes. This research emphasizes the importance of controlling anthropogenic emissions in relieving summer secondary pollution in the NCP region.摘要自2013年起, 中国空气质量虽改善, 但华北平原(NCP)重污染仍存在且二次污染加剧, 而人们对其成因和变化了解有限. 本研究利用2018–2022年数据, 借助CMAQ模型探讨此污染响应. 结果显示, 在2018–2022年间, PM2.5浓度显著下降31%–37%, O3和NO2的年下降速率分别为1%和0.5%. SIA和SOA也显著减少, 每年分别减少9%和6%. PM2.5主要因排放减少而下降, 而O3则受气象影响而波动. 硫酸盐和铵下降的主因是减排, 而硝酸盐对气象变化敏感. 排放和气象变化对SOA的总体减少同样重要, 但人为SOA对排放控制敏感, 生物SOA易受气象变化影响. 研究强调了控制人为排放对缓解NCP地区夏季二次污染的重要性.

Scenario-based statistical analysis for PM2.5 concentration: A case study of Seoul, South Korea

Rapid urban expansion and active economic development significantly contribute to increased levels of PM2.5, which degrades the urban environment. This study aims to establish a foundation for practical policy guidance on the impact of urban factors on PM2.5 concentrations in Seoul, South Korea. To achieve this objective, we utilized various urban factors, including socio-demographic data, three-dimensional urban geometry, land use, and traffic volume, in conjunction with PM2.5 concentrations estimated by satellite images. According to our statistical models, all urban development-related factors, except for Forest and Open Space (FOS), showed a statistically significant increase in PM2.5 concentration. However, FOS demonstrated an effective mitigation capability, with a 1% increase in FOS corresponding to an approximate 5% decrease in PM2.5 levels. In addition, five scenario analyses based on statistical results were conducted to evaluate the potential reduction of PM2.5 and associated spatial implications resulting from the removal of batching plants, decreased traffic volume, and the substitution of transportation network with green spaces in Seoul. They highlighted the importance of reducing traffic and increasing green spaces in order to lower PM2.5 levels. The findings suggest that a multifaceted strategy is necessary to develop efficient policies for mitigating PM2.5 for a sustainable environment.

Changes in source specific PM2.5 from 2010 to 2019 in New York and New Jersey identified by dispersion normalized PMF

Since the Clean Air Act amendments of 1970, various efforts have been made in the United States to control ambient particulate matter and improve air quality. Although substantial progress had been made by the end of the 1990s, further reductions were needed to meet the National Ambient Air Quality Standards. To assess the effectiveness of the regulations and the impacts of economic drivers, we investigated the PM2.5 source trends at 11 sites in New York and New Jersey for the 2010–2019 period. Dispersion-normalized positive matrix factorization (DN-PMF) was used to reduce the meteorological impacts on the source apportionment. The Theil-Sen nonparametric estimator and piecewise linear regression were applied to the resolved source contributions to quantify the trends. While there is a consistent overall decrease of PM2.5 at all sites, increases were observed for some specific PM sources. Secondary Sulfate increased after 2017 at most sites based on the piecewise analysis. Similarly, recent-year increases were resolved for Diesel concentrations likely due to increasing vehicle miles traveled for heavy-duty vehicles. Back trajectory analysis of the OP-Rich source identified southeastern U.S. as a likely major source area, while Biomass Burning has a mixed source from local domestic heating as well as transported from proscribed and wildfires in the southeastern U.S.