PM10 Pollution Research Articles

Sub-grid Variability and its Impact on Exposure in Regional Scale Air Quality and Integrated Assessment Models: Application of the uEMEP Downscaling Model

Regional scale air quality and integrated assessment models are necessarily limited in their spatial resolution, particularly when applied to larger regions such as Europe or for global applications. The EMEP MSC-W chemical transport model and the GAINS integrated assessment model, which makes use of source receptor information precalculated by the EMEP MSC-W model, use resolutions of 0.1°×0.1° and 0.3°×0.2°. These resolutions cannot account for variability at finer scales. Variability within grids, both concentration and population distributions, can be significant. To improve exposure calculations that take into account the sub-grid variability, the uEMEP model has been applied in this study to provide suitable parameterisations of the sub-grid variability within an EMEP MSC-W grid. The uEMEP model is applied on a European domain at 250 × 250 m2. This provides roughly 7000 sub-grids within each EMEP MSC-W 0.3°×0.2° grid to analyse the sub-grid variability in exposure. The analysis derives an exposure correction factor (ECF) for each source sector in each EMEP MSC-W grid. This factor provides information on the impact of sub-grid resolution on the population weighted concentrations within each grid. The population weighted standard deviation is also calculated, providing information on sub-grid variability to derive frequency distributions and parameterised forms of the sub-grid variability suitable for health risk assessments. The impact of sub-grid variability is assessed for the pollutants PM2.5, NOX and NO2. For NO2, additional chemistry calculations are carried out on the NOX frequency distributions. We show that the parameterised form of the sub-grid variability provides close to equivalent results as the original uEMEP calculations. Exposure to PM2.5 increases by around 13% when the sub-grid variability is taken into account for a 2030 emission scenario. For NO2, this increase is around 31% for the same scenario. A similar increase is found in the health risk indicator ’population attributable fraction’ when no lower concentration threshold is used in the concentration–response function. When applying the World Health Organizations recommended threshold value we find that including the sub-grid variability increases the calculated population attributable fraction by 32% and 46% for PM2.5 and NO2 respectively. We conclude that sub-grid variability should be taken into account in future exposure calculations for regional scale modelling and impact assessments.

Air Pollution and Covid-19

Air quality stands out as an important determinant of health, as its degradation was associated with around 4.2 million premature deaths in 2019, primarily due to heart and respiratory problems. It is shown that the elderly, the children, and individuals with pre-existing health conditions are simultaneously more sensitive to the impacts of air pollution and Covid-19, due to their fragile immune systems. Scientific evidence has shown the consequences of exposure to air pollutants to respiratory system diseases, emphasizing that it could be an important factor in explaining the spatial pattern of Covid-19 incidence and mortality. The aim of this study is to analyze the spatial association between air pollutant PM₂.₅ and the incidence and mortality of Covid-19 during March and December of 2020 in mainland Portugal. Weighted geographical models (GWR), were applied to identify and understand patterns, as well as explanatory factors in this relationship. The results obtained through the GWR models reveal that the pollutant PM₂.₅ has an association that varies in space. The incidence rate is higher in the southern, central, and northern regions of the country. The results of this study contribute to the analysis and assessment of the impact of air pollutants on human health, specifically in relation to health outcomes associated with Covid-19. It became evident that the concentration of PM₂.₅ is an important factor in explaining Covid-19 incidence rate in Portugal.

Air pollutants, genetic susceptibility, and incident schizophrenia in later life: A prospective study in the UK Biobank

ObjectiveAir pollution has been linked to multiple psychiatric disorders, but little is known on its long-term association with schizophrenia. The interaction between air pollution and genetic susceptibility on incident schizophrenia has never been reported. We aimed to explore the associations between long-term air pollution exposure and late-onset schizophrenia and evaluate whether genetic susceptibility could modify the association. MethodsThis population-based prospective cohort study included 437,802 middle-aged and elderly individuals free of schizophrenia at baseline in the UK Biobank. Land use regression models were applied in the estimation of the annual average concentrations of nitrogen dioxide (NO2), nitrogen oxides (NOx), fine particulate matter (PM2.5), and inhalable particulate matter (PM10) at residence. The associations between air pollutants and schizophrenia were evaluated by using Cox proportional hazard models. A polygenic risk score of schizophrenia was constructed for exploring potential interaction of air pollutants with genetic susceptibility. ResultsAn interquartile range increase in PM2.5, PM10, NO2, and NOx was associated with the hazard ratios (HR) for incident schizophrenia at 1.19, 1.16, 1.22, and 1.09, respectively. The exposure-response curves for the association of air pollution with incident schizophrenia were approximately linear. There are additive interactions of air pollution score (APS), PM10, NO2, and NOx with genetic risk. Specifically, compared with participants with low genetic susceptibility and low APS, the HR was 3.23 for individuals with high genetic risk and high APS, among which 0.49 excess risk could be attributed to the additive interaction, accounting for 15 % of the schizophrenia risk. ConclusionThis large-scale, prospective cohort study conveys the first-hand evidence that long-term air pollution exposure could elevate schizophrenia incidence in later life, especially for individuals with higher genetic risks. The findings highlight the importance of improving air quality for preventing the late-onset schizophrenia in an aging era, especially among those with high genetic risks.

Comprehensive analysis of air pollution and the influence of meteorological factors: a case study of adiyaman province.

Adıyaman, a city recently affected by an earthquake, is facing significant air pollution challenges due to both anthropogenic activities and natural events. The sources of air pollution have been investigated using meteorological variables. Elevated southerly winds, especially prominent in spring and autumn, significantly contribute to dust transport, leading to a decline in local air quality as detected by the HYSPLIT model. Furthermore, using Suomi-NPP Thermal Anomaly satellite product, it is detected and analyzed for crop burning activities. Agricultural practices, including stubble burning, contribute to the exacerbation of PM10 pollution during the summer months, particularly when coupled with winds from all directions except the north. In fall and winter months, heating is identified as the primary cause of pollution. The city center located north of the station is the dominant source of pollution throughout all seasons. The study established the connection between air pollutants and meteorological variables. Furthermore, the Spearman correlation coefficients reveal associations between PM10 and SO2, indicating moderate positive correlations under pressure conditions (r = 0.35, 0.52). Conversely, a negative correlation is observed with windspeed (r = -0.35, -0.50), and temperature also exhibits a negative correlation (r = -0.39, -0.54). During atmospheric conditions with high pressure, PM10 and SO2 concentrations are respectively 41.2% and 117.2% higher. Furthermore, pollutant concentration levels are 29.2% and 53.3% higher on days with low winds. Last, practical strategies for mitigating air pollution have been thoroughly discussed and proposed. It is imperative that decision-makers engaged in city planning and renovation give careful consideration to the profound impact of air pollution on both public health and the environment, particularly in the aftermath of a recent major earthquake.

Assessing the effect of COVID 19 lockdowns on the composition of organic compounds and potential source of PM2.5 in Hanoi, Vietnam.

The ambient air quality during COVID-19 lockdowns has been improved in many cities in the world. This study is to assess the changes in persistent organic pollutants in PM2.5 during the COVID-19 lockdown in Hanoi. Individual organic species in PM2.5 ((e.g., polycyclic aromatic hydrocarbons (PAHs), polychlorobiphenyls (PCBs), and organochlorine pesticides (OCPs)) were measured in an urban residential area in Hanoi from before the March 10th to April 22nd, 2020, including before the partial lockdown (BL) and the partial lockdown (PL) phases. During the PL phase, the concentration of Σ14PAHs and Σ28PCBs was reduced by 38 and 52% compared with the BL period, respectively. The diagnostic ratio method implied that the sources of PAHs within the PL phase had a less effect on traffic and industrial activities than in the BL phase. The characteristic ratio method indicated that PCBs were mixed by commercial product and combustion process in both the BL and the PL periods, however, the source of PCBs in the BL phase was influenced by municipal waste incineration more than those in the PL phase. The decreasing concentration of Σ20OCPs during the partial lockdown was attributed to the restriction of human activities during the quarantine period. The results suggested that the source of OCPs was probably derived from the usage of pesticides in current and, historical degradation or the transportation of pesticides from the soil to the atmosphere.

Understanding secondary particles in a regional site of Yangtze River Delta: Insights from mass spectrometric measurement

Submicron particulate matter (PM1) poses significant risks to health risks and global climate. In this study, secondary organic aerosols (SOA) and inorganic compositions were examined for their physicochemical characteristics and evolution using high-resolution aerosol instruments in Changzhou over one-month period. The results showed that transport accompanied by regional static conditions leaded to the occurrence of heavy pollution. In addition, regional generation and local emissions also leaded to the occurrence of light and moderate pollution during the observation period in Changzhou. Organic aerosols (OA) and nitrate (NO3−) accounted for 45 % and 23 % of PM1, respectively. The increase in PM1 was dominated by the contribution of NO3− and OA. SOA was dominance in OA (63 % with 40 % MO-OOA), which was higher than primary organic aerosols (POA). Besides, photochemical reactions and the high oxidizing nature of the urban atmosphere promoted the production of OA, especially MO-OOA in Changzhou. Our results highlight that secondary particles contribute significantly to PM pollution in Changzhou, underlining the importance of controlling emissions of gaseous precursors, especially under high oxidation conditions.

Visualising Daily PM10 Pollution in an Open-Cut Mining Valley of New South Wales, Australia—Part I: Identification of Spatial and Temporal Variation Patterns

The Upper Hunter Valley is a major coal mining area containing approximately 40% of the currently identified total coal reserves in New South Wales (NSW), Australia. Due to the ongoing increase in mining activities, PM10 (airborne particles with an aerodynamic diameter of less than 10 micrometres) pollution has become a major air quality concern in local communities. This paper summarises the spatial and temporal variability modes of PM10 pollution in the region, based on long-term multi-site monitoring data and the application of the rotated principal component analysis (RPCA) and wavelet analysis techniques. RPCA identified two distinct air quality clusters/subregions in the valley: one in the west/northwest and the other in the southeast. Wavelet analysis revealed the annual cycle to be the most persistent temporal mode of PM10 variability in both subregions, with intermittent signals also observed at time scales of around 120, 30~90, and under 30 days. How these variation modes are related to the effects of local PM10 emissions and the influence of meteorology at different time scales deserves further attention in future work. The findings will be used in air quality reporting and forecasting in NSW. The methodology and results can also be useful for air quality research in similar regions elsewhere.

Decoding temporal patterns and trends of PM10 pollution over Delhi: a multi-year analysis (2015-2022).

The current study delved into an extensive analysis of multi-year observations on PM10 to have trends at various time scales in Delhi, India. High-resolution ground observations from all 37 monitoring stations from 2015 to 2022 were used. This study used non-parametric generalized additive model (GAM) based smooth-trend and Theil-Sen slope estimator techniques to analyze temporal trends and variations. The long-term PM10 concentration, both in its ambient and de-seasonalized forms, exhibited a statistically significant decreasing trend. An average decrease of - 7.57 [95% confidence interval (CI) - 16.51, 0.18] µg m-3year-1 for ambient PM10 and - 8.45 [95% CI - 11.96, - 5.58] µg m-3year-1 for de-seasonalized PM10 mass concentration was observed. Breaking it down into seasons, we observed significant declines in PM10 concentrations during monsoon (- 10.71µgm-3year-1, p < 0.1) and post-monsoon (- 7.49µgm-3year-1, p < 0.001). On the other hand, summer and winter displayed statistically insignificant declining trends of - 5.32µgm-3year-1 and - 6.06µgm-3year-1, respectively. Remarkably, all months except March displayed declining PM10 concentrations, suggesting a gradual reduction in particle pollution across the city. Further analysis of PM10 across various wind sectors revealed a consistent decreasing trend in all wind directions. The most substantial decrease was observed from the northwest (- 10.24µgm-3year-1), while the minimum reduction occurred from the east (- 5.67µgm-3year-1). Throughout the 8-year study period, the daily average PM10 concentration remained at 228 ± 124µgm-3, ranging from 33 to 819µgm-3. Seasonal variations were apparent, with concentrations during winter, summer, monsoon, and post-monsoon seasons averaging 279 ± 133, 224 ± 117, 135 ± 95, and 323 ± 142µgm-3, respectively. November had the highest and August had the lowest concentration. Weekend PM10 concentration is slightly lower than weekdays. These findings emphasize the need for more stringent government action plans.

Spatial synergistic effect of urban green space ecosystem on air pollution and heat island effect

It is crucial for urban greening studies to comprehend the spatial synergy effect of the urban green space ecosystem on air pollution and the heat island effect. However, there are limited studies that have considered the impact of green space ecosystems on different air pollutants and urban heat island effects across different seasons from an urban space perspective. Therefore, this study took an urban space perspective and established a high-precision spatial grid with a resolution of 500 m. The spatial econometric regression and bivariate local Moran's I test were employed to analyze the impacts of urban green space ecosystems on air pollutants (including summer ozone (O3) concentration, winter fine particulate matter (PM2.5) concentration and winter inhalable particulate matter (PM10) concentration) and heat islands in summer and winter.The results showed that the spatial regression coefficients between green space and heat islands, PM2.5 concentration and PM10 concentration are significantly negative, −0.00049***, −0.00329***, and −0.00365*, respectively. The urban green space ecosystems are observed to effectively mitigate the heat island effect and help to reduce PM2.5 and PM10 pollution in winter. There are 88 high-high clustering in the spatial interactions between green space and ozone concentration in summer, that is, the grids with high O3 concentration surrounded by the high green space grids. It implies that urban green space ecosystems can significantly increase O3 pollution in summer. This study reveals that low BVOCs tree species should be widely planted in urban green spaces, while large-scale green space parks should be established in the urban region to continuously increase the proportion of green space. This study would contribute to improving the awareness of the government and relevant employees on scientific urban greening methods.

Molecular-Level Insights into the Relationship between Volatility of Organic Aerosol Constituents and PM2.5 Air Pollution Levels: A Study with Ultrahigh-Resolution Mass Spectrometry.

Volatility of organic aerosols (OAs) significantly influences new particle formation and the occurrence of particulate air pollution. However, the relationship between the volatility of OA and the level of particulate air pollution (i.e., particulate matter concentration) is not well understood. In this study, we compared the chemical composition (identified by an ultrahigh-resolution Orbitrap mass spectrometer) and volatility (estimated based on a predeveloped parametrization method) of OAs in urban PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) samples from seven German and Chinese cities, where the PM2.5 concentration ranged from a light (14 μg m-3) to heavy (319 μg m-3) pollution level. A large fraction (71-98%) of compounds in PM2.5 samples were attributable to intermediate-volatility organic compounds (IVOCs) and semivolatile organic compounds (SVOCs). The fraction of low-volatility organic compounds (LVOCs) and extremely low-volatility organic compounds (ELVOCs) decreased from clean (28%) to heavily polluted urban regions (2%), while that of IVOCs increased from 34 to 62%. We found that the average peak area-weighted volatility of organic compounds in different cities showed a logarithmic correlation with the average PM2.5 concentration, indicating that the volatility of urban OAs increases with the increase of air pollution level. Our results provide new insights into the relationship between OA volatility and PM pollution levels and deepen the understanding of urban air pollutant evolution.

Investigating the multiscale associations between urban landscape patterns and PM1 pollution in China using a new combined framework

Urban landscape patterns serve as the spatial carriers of emission sources and influencing factors of air contaminants, which inevitably impact submicron particle (PM1) pollution. However, most studies ignore the spatial nonstationary and nonlinear effects of urban landscape patterns on the PM1 concentration. In this study, a novel framework was developed that integrates a multiscale geographically weighted regression (MGWR) model and Shapley Additive exPlanations (SHAP) machine learning method to explore the spatial heterogeneity effect, relative contribution and nonlinear impact of landscape patterns on the PM1 concentration at the national and urban agglomeration scales. The results indicated that urban landscape patterns were closely related to PM1 pollution and exhibited obvious spatial differences. The disorderly expansion of urban built-up areas and an irregular urban morphology could aggravate PM1 pollution, while urban fragmentation and connectivity could impose reducing effects. Dispersed urban landscape patterns could reduce the PM1 concentration in the Yangtze River Delta (YRD), Pearl River Delta (PRD), and Chengdu-Chongqing (CDCQ) regions, whereas compact and continuous landscape patterns positively affected PM1 pollution mitigation in the Beijing-Tianjin-Hebei (BTH) and Guanzhong Plain (GZH) urban agglomerations. The impact of urban landscape patterns on PM1 pollution was greater in urban agglomerations than elsewhere. The ENN_MN landscape index exhibited the highest feature importance and interpretability. The threshold effects between the urban shape indicators and PM1 concentration were more complex than those with the other landscape indices. This critical knowledge provides a scientific basis for further understanding the correlation mechanism between PM1 pollution and the landscape pattern, urban sustainable planning and air pollution control.

Modeling the drivers of fine PM pollution over Central Europe: impacts and contributions of emissions from different sources

Abstract. Fine particulate matter (PM2.5) is among the air pollutants representing the most critical threat to human health in Europe. For designing strategies to mitigate this kind of air pollution, it is essential to identify and quantify the sources of its components. Here, we utilized the regional chemistry transport model CAMx (Comprehensive Air Quality Model with Extensions) to investigate the relationships between emissions from different categories and the concentrations of PM2.5 and its secondary components over Central Europe during the period 2018–2019, both in terms of the contributions of emission categories calculated by the particle source apportionment technology (PSAT) and the impacts of the complete removal of emissions from individual categories (i.e., the zero-out method). During the winter seasons, emissions from other stationary combustion (including residential combustion) were the main contributor to the domain-wide average PM2.5 concentration (3.2 µg m−3), and their removal also had the most considerable impact on it (3.4 µg m−3). During the summer seasons, the domain-wide average PM2.5 concentration was contributed the most by biogenic emissions (0.57 µg m−3), while removing emissions from agriculture–livestock had the most substantial impact on it (0.46 µg m−3). The most notable differences between the contributions and impacts for PM2.5 were associated with emissions from agriculture–livestock, mainly due to the differences in nitrate concentrations, which reached up to 4.5 and 1.25 µg m−3 in the winter and summer seasons, respectively. We also performed a sensitivity test of the mentioned impacts on PM2.5 on two different modules for secondary organic aerosol formation (SOAP and VBS), which showed the most considerable differences for emissions from other stationary combustion (in winter) and road transport (in summer).

The use of honey bees (Apis mellifera L.) to monitor airborne particulate matter and assess health effects on pollinators.

The honey bee Apis mellifera has long been recognized as an ideal bioindicator for environmental pollution. These insects are exposed to pollutants during their foraging activities, making them effective samplers of environmental contaminants, including heavy metals, pesticides, radionuclides, and volatile organic compounds. Recently, it has been demonstrated that honey bees can be a valuable tool for monitoring and studying airborne PM pollution, a complex mixture of particles suspended in the air, known to have detrimental effects on human health. Airborne particles attached to the bees can be characterised for their morphology, size, and chemical composition using a scanning electron microscopy coupled with X-ray spectroscopy, thus providing key information on the emission sources of the particles, their environmental fate, and the potential to elicit inflammatory injury, oxidative damage, and other health effects in living organisms. Here, we present a comprehensive summary of the studies involving the use of honey bees to monitor airborne PM, including the limits of this approach and possible perspectives. The use of honey bees as a model organism for ecotoxicological studies involving pollutant PM is also presented and discussed, further highlighting the role of the bees as a cornerstone of human, animal, and environmental health, according to the principles of the "One Health" approach.

Temporal variation of the PM2.5/PM10 ratio and its association with meteorological factors in a South American megacity: Metropolitan Area of Lima-Callao, Peru.

The Metropolitan Area of Lima-Callao (MALC) is a South American megacity that has suffered a serious deterioration in air quality due to high levels of particulate matter (PM2.5 and PM10). Studies on the behavior of the PM2.5/PM10 ratio and its temporal variability in relation to meteorological parameters are still very limited. The objective of this study was to analyze the temporal trends of the PM2.5/PM10 ratio, its temporal variability, and its association with meteorological variables over a period of 5years (2015-2019). For this, the Theil-Sen estimator, bivariate polar plots, and correlation analysis were used. The regions of highest mean concentrations of PM2.5 and PM10 were identified at eastern Lima (ATE station-41.2µg/m3) and southern Lima (VMT station-126.7µg/m3), respectively. The lowest concentrations were recorded in downtown Lima (CDM station-16.8µg/m3 and 34.0µg/m3, respectively). The highest average PM2.5/PM10 ratio was found at the CDM station (0.55) and the lowest at the VMT station (0.27), indicating a predominance of emissions from the vehicular fleet within central Lima and a greater emission of coarse particles by resuspension in southern Lima. The temporal progression of the ratio of PM2.5/PM10 showed positive and highly significant trends in northern and central Lima with values of 0.03 and 0.1 units of PM2.5/PM10 per year, respectively. In the southern region of Lima, the trend was also significant, showcasing a value of 0.02 units of PM2.5/PM10 per year. At the hourly and monthly level, the PM2.5/PM10 ratio presented a negative and significant correlation with wind speed and air temperature, and a positive and significant correlation with relative humidity. These findings offer insights into identifying the sources of PM pollution and are useful for implementing regulations to reduce air emissions considering both anthropogenic sources and meteorological dispersion patterns.

The effect of workplace mobility on air pollution exposure inequality—a case study in the Central Belt of Scotland

A large number of epidemiological studies have identified air pollution as a major risk to human health. Exposures to the pollutants PM2.5, NO2 and O3 cause cardiovascular and respiratory diseases, cancer and premature mortality. Whilst previous studies have reported demographic inequalities in exposure, with the most deprived and susceptible often being disproportionately exposed to the highest pollutant concentrations, the vast majority of these studies have quantified exposure based only on individuals’ place of residence. Here we use anonymised personal data from UK Census 2011, and hourly modelled air pollution concentrations at 0.8 km × 1.4 km spatial resolution in the Central Belt of Scotland, to investigate how inclusion of time spent at place of work or study affects demographic inequalities in exposure. We split the population by sex, ethnic group, age and socio-economic status. Exposure gradients are observed across all demographic characteristics. Air pollution exposures of males are more affected by workplace exposures than females. The White ethnic group has the lowest exposures to NO2 and PM2.5, and highest to O3. Exposures to NO2 and PM2.5 tend to peak between the ages of 21 and 30, but those aged 31–50 tend to be most impacted by the inclusion of time spent at workplace in the exposure assessment. People in the two least deprived deciles consistently have the lowest residential-only and combined residential-workplace exposure to NO2 and PM2.5, but experience the highest increase in exposure when including workplace. Overall, including workplace exposure results in relatively small change in median exposure but attenuates some of the exposure inequalities associated with ethnicity and socioeconomic status observed in exposure assessments based only on place of residence.

A novel framework for high resolution air quality index prediction with interpretable artificial intelligence and uncertainties estimation

Accurate air quality index (AQI) prediction is essential in environmental monitoring and management. Given that previous studies neglect the importance of uncertainty estimation and the necessity of constraining the output during prediction, we proposed a new hybrid model, namely TMSSICX, to forecast the AQI of multiple cities. Firstly, time-varying filtered based empirical mode decomposition (TVFEMD) was adopted to decompose the AQI sequence into multiple internal mode functions (IMF) components. Secondly, multi-scale fuzzy entropy (MFE) was applied to evaluate the complexity of each IMF component and clustered them into high and low-frequency portions. In addition, the high-frequency portion was secondarily decomposed by successive variational mode decomposition (SVMD) to reduce volatility. Then, six air pollutant concentrations, namely CO, SO2, PM2.5, PM10, O3, and NO2, were used as inputs. The secondary decomposition and preliminary portion were employed as the outputs for the bidirectional long short-term memory network optimized by the snake optimization algorithm (SOABiLSTM) and improved Catboost (ICatboost), respectively. Furthermore, extreme gradient boosting (XGBoost) was applied to ensemble each predicted sub-model to acquire the consequence. Ultimately, we introduced adaptive kernel density estimation (AKDE) for interval estimation. The empirical outcome indicated the TMSSICX model achieved the best performance among the other 23 models across all datasets. Moreover, implementing the XGBoost to ensemble each predicted sub-model led to an 8.73%, 8.94%, and 0.19% reduction in RMSE, compared to SVM. Additionally, by utilizing SHapley Additive exPlanations (SHAP) to assess the impact of the six pollutant concentrations on AQI, the results reveal that PM2.5 and PM10 had the most notable positive effects on the long-term trend of AQI. We hope this model can provide guidance for air quality management.

The effect of dust storms on air quality and public health in the city of Hillah

Dust storms, which have become frequent events, pose great risks to health and the environment. There are three factors responsible for generating them: strong winds, lack of rain, and lack of vegetation cover. AirQ+ software developed by the World Health Organization, was used to assess the health risks resulting from short-term exposure to PM2.5 measured using Continuous Particulate Monitor BAM-1020 for a period of two years from Jan 2021 to Jan 2023. The frequency and intensity of dust storms were recorded during the summer and spring of 2022, with high concentrations. The concentrations of the pollutant PM2.5 range during storm days (213.89 - 674.48 μg/m3), where short-term exposure causes an increase in the risk of respiratory illnesses and cardiovascular diseases. The most severe dust storms (674.48 μg/m3) caused a rise in the risk relative to 3.395 for respiratory diseases and 1.801 for cardiovascular disease. The great danger of dust storms is evident by comparing them with corresponding days for the year 2021, where the relative risk did not exceed 1.03for respiratory diseases and 1.01) for cardiovascular diseases. In general, air quality was classified using the concentrations obtained, where the concentrations of PM2.5 increased strongly, especially during the dry months, and the sand and dust storms that happened in 2022 caused the air quality to become dangerous by 2%, very unhealthy by 6%, and unhealthy by 8%. As for the other pollutants (SO2, NO2, and CO), they did not rise much beyond the allowable limit, and it has no direct relationship with dust storms, i.e. they did not increase with their increase. Several health and safety precautions and environmental control techniques can be used to lessen the effects of sand and dust storms. Although it is unreasonable to take action to stop or prevent widespread sand and dust storms from occurring, management measures can be taken to lessen their effects.

Association between Air Pollution Relating to Agricultural Residue Burning and Morbidity of Acute Cardiopulmonary Diseases in Upper Northern Thailand

In Northern Thailand, increasing seasonal agricultural residue burning has led to public concern about health risks. This study aimed to examine the associations between air pollutants related to agricultural residue burning and morbidity from acute cardiopulmonary diseases in upper Northern Thailand in 2018. An ecological study was conducted. Emergency room visits and hospitalizations for chronic obstructive pulmonary disease (COPD), stroke, myocardial infarction (MI), and asthma were extracted from the National Electronic Health Record database. We interpolated air pollution data to estimate weekly pollutant concentrations, including PM10, PM2.5, carbon monoxide, nitrogen dioxide, sulfur dioxide and ozone from 1 Jan to 31 Dec 2018. Associations between air pollution and health outcomes were analyzed using a mixed effect model incorporating different lag structures. Overall pollutant concentrations exceeded WHO air quality standard levels throughout March and April, which is the end of forest burning prohibition campaign. Morbidity from COPD, stroke, MI and asthma slightly increased over March–April. For every increase in PM2.5 level of 10 μg/m3, the relative risk of COPD, stroke, MI and asthma 1 week later was 1.10 (95% CI 1.09–1.12), 1.06 (1.05–1.08), 1.06 (1.04–1.08) and 1.06 (1.01–1.12), respectively. The effects of agricultural residue burning should be highlighted and policies should be developed to deter this practice.

Accountability analysis of health benefits related to National Action Plan on Air Pollution Prevention and Control in China.

China is one of the largest producers and consumers of coal in the world. The National Action Plan on Air Pollution Prevention and Control in China (2013-2017) particularly aimed to reduce emissions from coal combustion. Here, we show whether the acute health effects of PM2.5 changed from 2013 to 2018 and factors that might account for any observed changes in the Beijing-Tianjin-Hebei (BTH) and the surrounding areas where there were major reductions in PM2.5 concentrations. We used a two-stage analysis strategy, with a quasi-Poisson regression model and a random effects meta-analysis, to assess the effects of PM2.5 on mortality in the 47 counties of BTH. We found that the mean daily PM2.5 levels and the SO42- component ratio dramatically decreased in the study period, which was likely related to the control of coal emissions. Subsequently, the acute effects of PM2.5 were significantly decreased for total and circulatory mortality. A 10 μg/m3 increase in PM2.5 concentrations was associated with a 0.16% (95% CI: 0.08, 0.24%) and 0.02% (95% CI: -0.09, 0.13%) increase in mortality from 2013 to 2015 and from 2016 to 2018, respectively. The changes in air pollution sources or PM2.5 components appeared to have played a core role in reducing the health effects. The air pollution control measures implemented recently targeting coal emissions taken in China may have resulted in significant health benefits.

Characteristics of sub-micron aerosols above the urban canopy in Beijing during warm seasons

To understand the characteristics of atmospheric pollution above the urban canopy in warm seasons, the characteristics of sub-micron aerosol (PM1) was studied based on high-altitude observations at the Beijing 325 m meteorological tower. The PM1 at 260 m was 34, 29 and 21 μg m−3 in May 2015, June 2015, and June 2017, respectively, indicating a reduction in PM1 pollution above the urban canopy. Meanwhile, an overall decrease was also observed in the concentrations of all PM1 chemical species (excluding Chl and BC) and organic aerosol (OA) factors. Previous instances of heavy haze in Beijing often coincided with high humidity and stagnant weather conditions. However, the heightened pollution episodes in June 2017 were accompanied by high wind speeds and low relative humidity. Compared to May 2015, the contribution of secondary components to PM1 in June 2017 was more prominent, with the total proportion of SNA (sulfate, nitrate, and ammonium) and more-oxidized oxygenated OA (MO-OOA) to PM1 increased by approximately 10 %. Secondary species of NH4NO3, (NH4)2SO4, and MO-OOA, as well as black carbon, collectively contributed the vast majority of aerosol extinction coefficient (bext), with the four species contributing a total of ≥96 % to bext at 260 m. Hydrocarbon-like OA, cooking OA, and less-oxidized oxygenated OA have undergone significant reductions, so continued emphasis on controlling local sources to reduce these three aerosol species and addressing regional sources to further mitigate overall aerosol species is imperative. In lower pollution situation, the diurnal variation of PM was smoother, and its pollution sources were more regionally uniform, which might be attributed to the reduced diversity and complexity in the physical and chemical processes in air pollution.