PM10 Pollution Research Articles

Characterization of air pollutant emissions from construction machinery in Beijing and evaluation of the effectiveness of control measures based on information code registration data.

Non-road mobile machinery (NRMM), mainly construction machinery, has a high emission intensity of air pollutants, significantly impacting urban air quality. Most previous estimates of NRMM emissions have employed a top-down approach mainly based on estimates of energy consumption, leading to large uncertainties. This study uses the information code registration data specified in the latest regulations to establish a bottom-up method for emission accounting to more precisely identify the characteristics of air pollutant emissions from construction machinery in Beijing in 2020. Moreover, the study evaluates the effectiveness of the implementation of the corresponding control measures in conjunction with the current situation of pollution control of NRMM in Beijing. The results show the following: (1) Based on the information code registration data, there are 37,000-based fuel construction machines, with excavators accounting for the largest proportion (56%), loaders and forklifts also accounting for large proportions (19% and 15%, respectively), representing the main types of construction machinery. (2) Information code registration data better reflect the actual situation of construction machinery emissions than the top-down method; the emissions of the main air pollutants NOx, PM2.5, and VOCs amount to 12,000 tons, 600 tons, and 1000 tons, respectively, which are overestimated to some extent by the top-down method. (3) Loaders and excavators have a large contribution to emissions, accounting for 80-91% of these three pollutants emissions; there is a large quantity of machinery classified into the China III standard, accounting for 64-68% of these pollutants emissions; the designation of low emission zones banning the use of high-emission machinery plays a positive role in pollution reduction, but high-emission machinery is still used in these regions, which requires further attention. (4) In the future, the scope of these regions banning high-emission machinery and the types of controlled machinery should be further expanded, and the supervision and enforcement should be strengthened, Furthermore, the structural adjustment and energy conservation of construction machinery should be promoted, and measures such as electrification should be implemented for part of the light construction machinery to continue to reduce pollutant emissions.

Laboratory investigation of pollutant emissions and PM2.5 toxicity of underground coal fires.

Widespread underground coal fires (UCFs) release large amounts of pollutants, thus leading to air pollution and health impacts. However, this topic has not been widely investigated, especially regarding the potential health hazards. We quantified the pollutant emissions and analyzed the physicochemical properties of UCF PM2.5 in a laboratory study of coal smoldering under a simulated UCF background. The emission factors of CO2, CO, and PM2.5 were 2489 ± 35, 122 ± 9, 12.90 ± 1.79 g/kg, respectively. UCF PM2.5 are carbonaceous particles with varied morphology and complex composition, including heavy metals, silica and polycyclic aromatic hydrocarbons (PAHs). The main PAHs components were those with 2-4 rings. Benzoapyrene (BaP) and indeno[1,2, 3-cd]pyrene (IcdP) were important contributors to the carcinogenesis of these PAHs. We quantitatively evaluate the toxicity of inhaled UCF PM2.5 using a nasal inhalation exposure system. The target organs of UCF PM2.5 are lungs, liver, and kidneys. UCF PM2.5 presented an enriched chemical composition and induced inflammation and oxidative stress, which together mediated multiple organ injury. Long-term PM2.5 metabolism is the main cause of persistent toxicity, which might lead to long-term chronic diseases. Therefore, local authorities should recognize the importance and effects of UCF emissions, especially PM2.5, to establish control and mitigation measures.

Оцінка впливу забруднення атмосферного повітря РМ2,5 на здоров’я населення м. Києва

Objective: Risk assessment for the health of the population in Kyiv from air pollution PM2.5. Materials and methods: There were 24826 measurements of PM2.5 obtained at a stationary observation post in Kyiv. Statistical indicators of noncommunicable morbidity in the adult population of Kyiv were used for the analysis. The attributive part of the risk of death and the quantity of attributable deaths were calculated using the software package AirQ +. Results: As a result of studies in Kyiv, the average value of daily concentrations of PM2.5 for 3 years was calculated 21.7 µg/m3. The attributive part of the risk of death among adults aged ≥30 years from lung cancer was determined to be 9.59% and among adults aged ≥25 years from chronic obstructive pulmonary disease to be 9.07%. Attributable deaths that were associated with long-term PM2.5 exposure were: 63 (95% CI: 29-93) deaths from lung cancer and 7 (95% CI: 4-10) deaths from chronic obstructive pulmonary disease. This represents 9.6% and 6.7% of all deaths, respectively.

Elucidating the Chemical Compositions and Source Apportionment of Multi-Size Atmospheric Particulate (PM10, PM2.5 and PM1) in 2019–2020 Winter in Xinxiang, North China

The pollution characteristics of multi-size atmospheric particulates in Xinxiang, which was one of the most polluted cities across China, are still unclear even through air quality in Xinxiang has been improved in recent years. PM10, PM2.5, and PM1 samples were synchronously collected from 21 December 2019 to 17 January 2020 to explore pollution levels and reveal sources of PM in Xinxiang. The average mass concentrations of PM10, PM2.5, and PM1 were as high as 155.53 μg m−3, 120.07 μg m−3, and 85.64 μg m−3 during the observation period, respectively. Almost all of the chemical compositions in PM10, PM2.5 and PM1 increased continuously and obviously with the aggravation of the pollution level. Compared with the clean period, the enhancement of sulfate (23–27%) in PM was obvious higher than nitrate (19–22%) during the pollution period, which demonstrated that sulfate was the main contributor to the high concentration of PM in this study. Similar source distributions for PM10, PM2.5, and PM1 were also found, including traffic source, combustion source, secondary aerosols, industrial source, and fugitive dust, by using the positive matrix factorization (PMF) model. Furthermore, the contributions of the combustion source and secondary aerosol were found to be higher in smaller particles (PM2.5 and PM1), while the contribution of fugitive dust was higher in PM10. Moreover, dust and sand were entrained by air masses from the northwest that increased the contribution of dust in PM at the observation site. The potential source contribution function (PSCF) analysis illustrated that regional emission sources in northern and eastern Xinxiang might be important potential contributors to PM pollution in Xinxiang.

Impacts of traffic-related particulate matter pollution on semen quality: A retrospective cohort study relying on the random forest model in a megacity of South China

BackgroundEmerging evidence shows the detrimental impacts of particulate matter (PM) on poor semen quality. High-resolution estimates of PM concentrations are conducive to evaluating accurate associations between traffic-related PM exposure and semen quality. MethodsIn this study, we firstly developed a random forest model incorporating meteorological factors, land-use information, traffic-related variables, and other spatiotemporal predictors to estimate daily traffic-related PM concentrations, including PM2.5, PM10, and PM1. Then we enrolled 1310 semen donors corresponding to 4912 semen samples during the study period from January 1, 2019, and December 31, 2019 in Guangzhou city, China. Linear mixed models were employed to associate individual exposures to traffic-related PM during the entire (0–90 lag days) and key periods (0–37 and 34–77 lag days) with semen quality parameters, including sperm concentration, sperm count, progressive motility and total motility. ResultsThe results showed that decreased sperm concentration was associated with PM10 exposures (β: -0.21, 95 % CI: −0.35, −0.07), sperm count was inversely related to both PM2.5 (β: -0.19, 95 % CI: −0.35, −0.02) and PM10 (β: -0.19, 95 % CI: −0.33, −0.05) during the 0–90 days lag exposure window. Besides, PM2.5 and PM10 might diminish sperm concentration by mainly affecting the late phase of sperm development (0–37 lag days). Stratified analyses suggested that PBF and drinking seemed to modify the associations between PM exposure and sperm motility. We did not observe any significant associations of PM1 exposures with semen parameters. ConclusionOur results indicate that exposure to traffic-related PM2.5 and PM10 pollution throughout spermatogenesis may adversely affect semen quality, especially sperm concentration and count. The findings provided more evidence for the negative associations between traffic-related PM exposure and semen quality, highlighting the necessity to reduce ambient air pollution through environmental policy.

Multi-class organic pollutants in atmospheric particulate matter (PM2.5) from a Southwestern Europe industrial area: Levels, sources and human health risk

The occurrence of 50 multi-class pollutants comprising 18 polycyclic aromatic hydrocarbons (PAHs), 12 phthalate esters (PAEs), 12 organophosphorus flame retardants (OPFRs), 6 synthetic musk compounds (SMCs) and 2 bisphenols was studied in atmospheric particulate matter (PM2.5) samples collected at an industrial area focused on automotive manufacturing located at the Southwestern Atlantic European region (Vigo city, Spain) during 1-year period. Among all quantitated pollutants in PM2.5 samples, bisphenol A (BPA) was the most predominant with an average concentration of 6180 pg m−3, followed by PAHs comprising benzo(b+j)fluoranthene (BbF + BjF) and benzo(g,h,i)perylene (BghiP), accounting for 546 pg m−3 and 413 pg m−3 respectively. In addition, two OPFRs concerning tris(chloropropyl) phosphate (TCPP) and triphenyl phosphine oxide (TPPO) were the next following the concentration order, accounting for 411 pg m−3 and 367 pg m−3 respectively; being butyl benzyl phthalate (BBP) the most profuse PAE (56.1 pg m−3 by average). High relative standard deviations (RSDs) were observed during the whole sampling period, while statistically significant differences were only observed for PAHs concentrations during cold and warm seasons. Furthermore, some water-soluble ions and metal(oid)s were analysed in PM2.5 samples to be used as PM source tracers, whose concentrations were quite below the target levels set in the current legislation. Data obtained from principal component analysis (PCA) and PAHs molecular indices suggested a pyrogenic and petrogenic origin for PAHs, whereas occurrence of the remaining compounds seems to be attributed to resources used in the automotive industrial activity settled in the sampling area. Moreover, although a substantial anthropogenic source to PM2.5 in the area was observed, marine and soil resuspension contributions were also accounted. Finally, carcinogenic and non-carcinogenic risks posed by PM2.5-bound pollutants inhalation were assessed, being both averages within the safe level considering the whole period.

Cumulative effects of air pollution and climate drivers on COVID-19 multiwaves in Bucharest, Romania

Over more than two years of global health crisis due to ongoing COVID-19 pandemic, Romania experienced a five-wave pattern. This study aims to assess the potential impact of environmental drivers on COVID-19 transmission in Bucharest, capital of Romania during the analyzed epidemic period. Through descriptive statistics and cross-correlation tests applied to time series of daily observational and geospatial data of major outdoor inhalable particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) or ≤ 10 µm (PM10), nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), Aerosol Optical Depth at 550 nm (AOD) and radon (222Rn), we investigated the COVID-19 waves patterns under different meteorological conditions. This study examined the contribution of individual climate variables on the ground level air pollutants concentrations and COVID-19 disease severity. As compared to the long-term average AOD over Bucharest from 2015 to 2019, for the same year periods, this study revealed major AOD level reduction by ~28 % during the spring lockdown of the first COVID-19 wave (15 March 2020–15 May 2020), and ~16 % during the third COVID-19 wave (1 February 2021–1 June 2021). This study found positive correlations between exposure to air pollutants PM2.5, PM10, NO2, SO2, CO and 222Rn, and significant negative correlations, especially for spring-summer periods between ground O3 levels, air temperature, Planetary Boundary Layer height, and surface solar irradiance with COVID-19 incidence and deaths. For the analyzed time period 1 January 2020–1 April 2022, before and during each COVID-19 wave were recorded stagnant synoptic anticyclonic conditions favorable for SARS-CoV-2 virus spreading, with positive Omega surface charts composite average (Pa/s) at 850 mb during fall- winter seasons, clearly evidenced for the second, the fourth and the fifth waves. These findings are relevant for viral infections controls and health safety strategies design in highly polluted urban environments.

Ensemble source apportionment of air pollutants and carbon dioxide based on online measurements

Air pollution and climate change have attracted worldwide attention due to their significant threats to human health and the environment. To maximize the co-benefits of clean air policies on greenhouse gas emissions and vice versa, it is imperative to coordinate emission control measures for air pollutants and carbon dioxide (CO2). To do this, we first need to better quantify the impacts of different sources to air pollutants and CO2 at once. Based on a 2-year observation of fine particulate matter (PM2.5), gaseous pollutants (including sulfur dioxide (SO2), nitrogen oxides (NOx), initial volatile organic compounds (In–VOCs)), as well as CO2, we apportioned the sources of criteria air pollutants and CO2 simultaneously. We then developed a new source apportionment method to quantitatively determine the relative impacts of different emission sources on (1) gaseous pollutants ensemble (SO2-NOx-In-VOCs) and (2) PM2.5 and CO2 ensemble (PM2.5-CO2). The results demonstrate that vehicle exhaust (35%), industrial emissions (31%), biomass burning (18%), and coal combustion (16%) were the main control targets for the SO2-NOx-In-VOCs ensemble, and they were also the dominant contributors to PM2.5-CO2 ensemble with similar contribution rates. Not surprisingly, the source impacts are substantially different for the two studied ensembles to those of individual air pollutant alone. This study provides a new source apportionment method to deliver scientific evidence for developing a coordinated strategy to maximize the benefits of clean air and carbon policies to air quality and the climate.

Real-Time Source Apportionment of PM2.5 Highlights the Importance of Joint Controls on Atmospheric Pollution in Cold Region of China

Harbin is a northmost megacity in the cold regions of China and experiences severe PM2.5 pollution. However, comprehensive investigations for severe haze formation are few. In this study, we simultaneously measured aerosol composition in real time to assess the sources apportionment, regional transport and its interaction with meteorology from 1 October 2018 to 1 May 2019 by using the single particle aerosol mass spectrometer (SPAMS). The daily average PM2.5 concentration was 51.21 µg/m3 with the hourly maximum of 900.45 µg/m3. Winter coal combustion was the largest source of PM2.5 aerosols during this period. Open straw burning from surrounding and adjacent areas by short-distance transport could aggravate air quality deterioration in Harbin. Three extreme haze events (i.e., Ep1, Ep2 and Ep3) were observed in this study, showing the typical characteristics of local winter pollution. The pollutants of PM2.5 and SO2 emitted from coal combustion played an important role in haze episode during Ep1, whereas Ep2 was caused by the joint effect of coal combustion and straw burning. Ep3 was characterized by long-distance transport of windblown dust from southeast Inner Mongolia and northwest Harbin. Real-time source apportionment of fine particulate matter highlights the importance of joint control of coal and straw burning from the surrounding cities of Harbin.

Emissions and Source Apportionment of PM2.5 and PM10 Pollution in Hyderabad, India

Emissions and Source Apportionment of PM2.5 and PM10 Pollution in Hyderabad, India

Comparison of two air quality models in complex terrain near sea shore

Air pollution is the most important environmental problem in Zonguldak, Turkey due to excessive coal combustion and thermal power plant emissions. The city center is located on a complex terrain near the Black Sea shore. There exist some previous studies about PM10 pollution in the study area, but none of them is related to the spatial distribution of the pollutant. This air quality modeling study aims to fill this gap in the literature. Firstly, PM10 emission inventory has been prepared for point, line, and area sources for the year 2011. For that period, bituminous coal was the principal fuel for domestic heating in houses and generating electricity in thermal power plants, therefore particulate matter (PM10) was the leading air pollutant. Emission inventory calculations revealed that 2710.2 tons of PM10 have been emitted to the atmosphere from all sources in the study area. Then, the air quality modeling has been performed for PM10 by using two air quality models: AERMOD and CALPUFF. According to the modeling results, the PM10 pollution levels may pose a health threat to the inhabitants of Zonguldak. The maximum PM10 concentrations predicted by the CALPUFF model were higher than that of the AERMOD model. The model predicted values plus background concentration were validated against the PM10 measurements by using fractional bias, index of agreement, geometric mean bias, and geometric mean-variance. According to the model performance analysis, CALPUFF showed slightly better performance as compared to AERMOD.

Methods for calculating the pollutants dispersion in the urban atmosphere

Introduction. The important peculiarity of urban ecology is air dusting with pollutants PM2.5 formed in traffic dangerous for health of city-dwellers. The work aim is the comparative analysis of methods and techniques of pollutants dispersion calculation (in general and as applied to PM2.5) in the city atmosphere, based on the Gaussian models, simulation models, models with partial derivative equations and statistical models.
 Materials and methods. Analytical generalization and systematization of the experience of domestic and foreign studies set forth in the research literature were applied.
 Results. Comparative analysis showed that most of the reviewed methods and techniques in the calculation of the pollutants dispersion takes into account only the parameters of the pollution source, which makes it possible to choose the correct location and capacity. However, for all the merits of all the methods and techniques do not take into account landscaping within the city, which according to recent research has a significant impact on reducing urban air pollution, including in relation to particles PM2.5.
 Conclusions. The next step towards the development of calculation methods and techniques for the dispersion of pollutants in the urban atmosphere in order to ensure the environmental safety of the urban environment is the development of a methodology that allows for the calculation of the concentration of pollutants to take into account the dust absorption capacity of plantings with respect to PM2.5 particles and sorption properties of the deciduous surface of trees and shrubs in the study area. This technique can determine the placement of landscaping objects (trees, shrubs), lawn urban landscaping and landscaping of roofs and stylobates of residential and public buildings in order to reduce PM2.5.

Exposure to ambient air pollution and osteoarthritis; an animal study

Exposure to air pollution has been associated with many adverse health effects. However, the evidence on the effects on osteoarthritis (OA) is scarce and the potential mechanism is unclear yet. Therefore, this study assessed the effect of exposure to air pollution (gaseous and particulate matter) and OA based on an animal model. We used four groups of female rats, including i) exposure to PMs and gaseous pollutants, ii) exposure only to gaseous pollutants, iii) exposure only to PMs, and iv) control (unexposed) group. The OA biomarkers, i.e., osteocalcin, cartilage oligomeric protein (COMP), and N-Telopeptides of Type I Collagen (NTX-I) and cytokines were measured in the plasma to detect the effect of exposure to ambient air pollution on OA in this animal model. The forced jogging exercises for 1h and 5 days per week were used to record the physical activities. The median (interquartile range) concentrations of PM2.5 and PM10 were 35.9 (15.4) and 47.5 (22.5) μg/m3, respectively. The median (interquartile range (IQR)) of PM2.5, PM10, CO, NO2, SO2 and O3 in the inlet ambient air were 36.9 (16.9), 51.7 (23.6) μg/m3, 16.1 (12.5) ppm, 413.7 (177.1), 334.2 (218.8) and 208.9 (113.1) ppb, respectively. The osteocalcin was significantly lower in PM as well as PM-gaseous exposure groups compared to control. Moreover, expressions of COMP were increased significantly in the PMs and exposure group compared to the control. For the PMs-gaseous exposure group, the COMP expressions were the highest compared to the control group. Similar results were observed for NTX-I. Exposure to PM and gaseous pollutants significantly increased plasma cytokine levels compared to control. Overall, our study showed a significant effect of exposure to PMs and PMs-gaseous exposure with OA in rats. Moreover, we observed a synergistic effect of mixed gaseous-PMs exposure compared to PMs and gaseous pollutants separately.

Urban Particulate Matter Hazard Mapping and Monitoring Site Selection in Nablus, Palestine

Few air pollution studies have been applied in the State of Palestine and all showed an increase in particulate matter concentrations above WHO guidelines. However, there is no clear methodology for selecting monitoring locations. In this study, a methodology based on GIS and locally calibrated low-cost sensors was tested. A GIS-based weighted overlay summation process for the potential sources of air pollution (factories, quarries, and traffic), taking into account the influence of altitude and climate, was used to obtain an air pollution hazard map for Nablus, Palestine. To test the methodology, eight locally calibrated PM sensors (AirUs) were deployed to measure PM2.5 concentrations for 55 days from 7 January to 2 March 2022. The results of the hazard map showed that 82% of Nablus is exposed to a high and medium risk of PM pollution. Sensors’ readings showed a good match between the hazard intensity and PM concentrations. It also shows an elevated PM2.5 concentrations above WHO guidelines in all areas. In summary, the overall average for PM2.5 in the Nablus was 48 µg/m3. This may indicate the effectiveness of mapping methodology and the use of low-cost, locally calibrated sensors in characterizing air quality status to identify the potential remediation options.

Magnetic characterisation of London's airborne nanoparticulate matter

Iron-bearing particulate matter produced by vehicle emissions is known to be toxic. To better quantify potential health risks, we have conducted the first magnetic study of a time-series of London's inhalable particulate matter (<10 μm, PM10), captured by three monitoring stations in central London (Marylebone Road, Earl's Court Road and Oxford Street) through 2010 and 2012. We conducted room-temperature analysis on all the samples, and a limited number of samples were analysed at both high and low temperatures. The high-temperature measurements identified magnetite as the dominant magnetic phase. The low-temperature measurements revealed high numbers of nanoparticles, which, assuming magnetite, are in the grain-size range 1–4 nm. It is estimated that as much as ∼40% of the total magnetic signal at 10 K is from particles <4 nm, that are magnetically ‘invisible’ at room-temperature and are being routinely under-estimated in room temperature-based magnetic studies. From the low-temperature measurements, the total concentration of magnetite was estimated at ∼7.5%, significantly higher than previously reported. The room-temperature magnetic data were compared with other pollution data, e.g., NOX and PM10, and meteorological data. Mass-dependent terms like the saturation magnetisation were found to display a strong correlation with NOX and PM10, indicating a common source for these pollutants, i.e., vehicle emissions. Magnetic coercivity measurements, which are independent of abundance, and provide information on grain-size, were consistent across all three sampling localities, again suggesting a major dominant source. Relatively small variations in coercivity were correlated with meteorological events, e.g., temperature and precipitation, suggesting preferential removal of larger airborne grains, i.e., >50 nm.

Air Quality Impacts of Shale Gas Development in Pennsylvania

We estimate the impact of shale gas development on particulate matter pollution using a quasi-experimental setting in Pennsylvania where some wells were developed to produce natural gas whereas other wells were permitted but not drilled. In doing so, we utilize a novel empirical approach drawing upon insights from atmospheric chemistry to account for windblown pollution spillovers in a difference-in-differences framework. Utilizing a high frequency, high resolution satellite-based measure of PM pollution between 2000 and 2018, we identify causal increases in PM2.5 concentration ranging from 0.017 μg/m3 to 0.062 μg/m3 in the vicinity of over 20,000 wells, resulting in approximately 20 additional deaths between 2010 and 2017.

The role of lipid profile in the relationship between particulate matters and hyperuricemia: A prospective population study

ContextRecent studies in specific population subgroups (e.g., pregnant women) have suggested PM exposure increases the risk of hyperuricemia. However, no studies have examined this in the general population. Furthermore, the underlying mechanism through which PM impacts hyperuricemia risk is poorly understood. ObjectiveTo assess the association between long-term exposure to PM and risk of hyperuricemia and whether this association is mediated by lipid profile. MethodsWe included 5939 participants in Southwest China from the China Multi-Ethnic Cohort (baseline 2018–2019, follow-up 2020–2021). Long-term PM pollutants (PM1, PM2.5, PM10) exposure for each individual was represented by the three-year average PM levels before the baseline survey. Hyperuricemia at follow-up was defined as the serum uric acid above 7.0 mg/dL in men and 6.0 mg/dL in women. Serum lipids were measured at baseline including total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). The association of PM with hyperuricemia was accessed through logistic regression. The potential mediation effects of serum lipids were evaluated through causal mediation analyses. ResultsA total of 837 participants were newly diagnosed with hyperuricemia. The odds ratios of hyperuricemia associated with an interquartile range (IQR) increase in PM1, PM2.5, and PM10 (IQR: 21.10, 25.78, 30.43 μg m−3) were 1.72 (95% CI: 1.23, 2.39), 2.68 (95% CI: 1.59, 4.49), and 1.81 (95% CI: 1.20, 2.72), respectively. The association between PM2.5, PM1, and PM10 on hyperuricemia was mediated by HDL-C (10%) and LDL-C (3%). ConclusionHigher particulate matter exposure was associated with higher hyperuricemia incidence. The decline in HDL-C and rise in LDL-C partially mediated this association. These findings were conducive to scientific research about the underlying mechanism of PM on hyperuricemia.

Impact of Climate-Driven Land-Use Change on O3 and PM Pollution by Driving BVOC Emissions in China in 2050

This study predicted three future land-use type scenarios in 2050 (including the Shared Socioeconomic Pathway SSP126, SSP585, and carbon scenario) based on the Land-Use Harmonization (LUH2) project and the future evolution of land-use types considering China’s carbon neutrality background. The contribution of land-use changes to terrestrial natural source biogenic volatile organic compounds (BVOCs), as well as O3 and PM concentrations, were determined. Under the SSP126 pathway, meteorological changes would increase BVOC emissions in China by 1.0 TgC in 2050, compared with 2015, while land-use changes would increase them by 1.5–7.1 TgC. The impact of land-use changes on O3 and PM concentrations would be less than 3.6% in 2050 and greater in summer. Regional differences must be considered when calculating future environmental background concentrations of pollutants. Due to more afforestation measures under the SSP126 scenario, the impact of land-use change on pollutants was more obvious under the SSP126 pathway than under the SSP585 pathway. Under the carbon scenario, the increase in PM concentration caused by land-use changes would pose a risk to air quality compliance; thus, it is necessary to consider reducing or offsetting this potential risk through anthropogenic emission control measures.

Spatiotemporal Distribution Patterns and Exposure Risks of PM2.5 Pollution in China

The serious pollution of PM2.5 caused by rapid urbanization in recent years has become an urgent problem to be solved in China. Annual and daily satellite-derived PM2.5 datasets from 2001 to 2020 were used to analyze the temporal and spatial patterns of PM2.5 in China. The regional and population exposure risks of the nation and of urban agglomerations were evaluated by exceedance frequency and population weight. The results indicated that the PM2.5 concentrations of urban agglomerations decreased sharply from 2014 to 2020. The region with PM2.5 concentrations less than 35 μg·m−3 accounted for 80.27% in China, and the average PM2.5 concentrations in 8 urban agglomerations were less than 35 μg·m−3 in 2020. The spatial distribution pattern of PM2.5 concentrations in China revealed higher concentrations to the east of the Hu Line and lower concentrations to the west. The annual regional exposure risk (RER) in China was at a high level, with a national average of 0.75, while the average of 14 urban agglomerations was as high as 0.86. Among the 14 urban agglomerations, the average annual RER was the highest in the Shandong Peninsula (0.99) and lowest in the Northern Tianshan Mountains (0.76). The RER in China has obvious seasonality; the most serious was in winter, and the least serious was in summer. The population exposure risk (PER) east of the Hu Line was significantly higher than that west of the Hu Line. The average PER was the highest in Beijing-Tianjin-Hebei (4.09) and lowest in the Northern Tianshan Mountains (0.71). The analysis of air pollution patterns and exposure risks in China and urban agglomerations in this study could provide scientific guidance for cities seeking to alleviate air pollution and prevent residents’ exposure risks.

ASSESSMENT OF AIR QUALITY IN MEDINIPUR TOWN DURING PRE-LOCK DOWN, LOCK DOWN AND POST LOCKDOWN PERIOD: A COMPARATIVE STUDY

Air Quality is most important factor for human and sustainability of environment. Therefore, this study allows to show the quality of air in Medinipur town in between the time period pre lock down, lock down and post lock down period. In this study noticed that the graph of Air Quality Index is become satisfying position in lock down period. In pre lock down period the AQI value of Medinipur city are above of 100 but in the lock down period AQI value become 40-60 and after the lockdown period the AQI value is slowly increase due to changing amount of NO2, PM10 and SO2 pollutant. Those changes plying signicant role of positive impact of lock down. Here mainly showing a comparative status of air quality in pre lock down, lock down and post lock down period in Medinipur town.