Air Pollution Control Research Articles

Causal association between long-term exposure to air pollution and incident Parkinson’s disease

Epidemiological evidence for long-term air pollution exposure and Parkinson’s disease (PD) is controversial, and analysis of causality is limited. We identified 293,888 participants who were free of PD at baseline in the UK Biobank (2006–2010). Time-varying air pollution [fine particulate (PM2.5) and ozone (O3)] exposures were estimated using spatio-temporal models. Incident cases of PD were identified using validated algorithms. Four methods were used to investigate the associations between air pollution and PD, including (1) standard time-varying Cox proportional-hazard model; (2) Cox models weighted by generalized propensity score (GPS) and inverse-probability weights (IPW); (3) instrumental variable (IV) analysis; and (4) negative control outcome analysis. During a median of 11.6 years of follow-up, 1822 incident PD cases were identified. Based on standard Cox regression, the hazard ratios (95% confidence interval) for a 1 µg/m3 or ppb increase in PM2.5 and O3 were 1.23 (1.17, 1.30) and 1.02 (0.98, 1.05), respectively. Consistent results were found in models weighted by GPS and IPW, and in IV analysis. There were no significant associations between air pollution and negative control outcomes. This study provides evidence to support a causal association between PM2.5 exposure and PD. Mitigation of air pollution could be a protective measure against PD.

Direct mitigation of secondary organic aerosol particulate pollutants by multiphase photocatalysis

Particulate matter represents one of the most severe air pollutants globally. Organic aerosol (OA) comprises 30–70 % of submicron particle mass in urban areas. An effective way to mitigate OA particulate pollutants is to reduce the formation of secondary organic aerosol (SOA). Here, we studied the effect of titanium dioxide (TiO2) photocatalytic seeds on the formation and mitigation of SOA particles from α-pinene or toluene oxidation in chamber. For the first time, we discovered that under ultraviolet (UV) irradiation, the presence of TiO2 directly removed internally mixed α-pinene SOA mass by 53.7 % within 200 mins, and also directly removed SOA matter in an externally mixed state that is not in direct contact with TiO2 surface: the mass of externally mixed α-pinene SOA was reduced by 21.9 % within 81 mins, and the toluene SOA mass was reduced by 46.6 % in 145mins. In addition, the presence of TiO2 effectively inhibited the formation of SOA particles with a SOA mass yield of zero. This study brings up an innovative concept for air pollution control - the direct photocatalytic degradation of OA with aid of TiO2-based photocatalysts. Our novel findings will potentially bring practical applications in air pollution abatement and regional, even global aerosol-climate interactions.

Hourly PM2.5 Concentration Prediction Based on Empirical Mode Decomposition and Geographically Weighted Neural Network

Accurate prediction of fine particulate matter (PM2.5) concentration is crucial for improving environmental conditions and effectively controlling air pollution. However, some existing studies could ignore the nonlinearity and spatial correlation of time series data observed from stations, and it is difficult to avoid the redundancy between features during feature selection. To further improve the accuracy, this study proposes a hybrid model based on empirical mode decomposition (EMD), minimal-redundancy-maximal-relevance (mRMR), and geographically weighted neural network (GWNN) for hourly PM2.5 concentration prediction, named EMD-mRMR-GWNN. Firstly, the original PM2.5 concentration sequence with distinct nonlinearity and non-stationarity is decomposed into multiple intrinsic mode functions (IMFs) and a residual component using EMD. IMFs are further classified and reconstructed into high-frequency and low-frequency components using the one-sample t-test. Secondly, the optimal feature subset is selected from high-frequency and low-frequency components with mRMR for the prediction model, thus holding the correlation between features and the target variable and reducing the redundancy among features. Thirdly, the residual component is predicted with the simple moving average (SMA) due to its strong trend and autocorrelation, and GWNN is used to predict the high-frequency and low-frequency components. The final prediction of the PM2.5 concentration value is calculated by an artificial neural network (ANN) composed of the predictive values of each component. PM2.5 concentration prediction experiments in three representational cities, such as Beijing, Wuhan, and Kunming were carried out. The proposed model achieved high accuracy with a coefficient of determination greater than 0.92 in forecasting PM2.5 concentration for the next 1 h. We compared this model with four baseline models in forecasting PM2.5 concentration for the next few hours and found it performed the best in PM2.5 concentration prediction. The experimental results indicated the proposed model can improve prediction accuracy.

Selection of municipal solid waste disposal technology using the Analytic Hierarchy Process and Genetic Algorithm for Gulf Cooperation Council Countries

Municipal Solid Waste (MSW) management represents a significant challenge in rapidly urbanizing regions, notably within the Gulf Cooperation Council (GCC) countries. This study focuses on Kuwait, exemplifying the GCC context, to identify the most suitable MSW disposal methods. We integrate the Analytic Hierarchy Process (AHP) with Genetic Algorithms (GA) for a comprehensive evaluation and optimization of disposal options, considering 13 criteria across economic, environmental, technical, and safety dimensions. Five disposal methods (landfilling, controlled dumpsites, composting, incineration, and refuse-derived fuel [RDF] combustion) are analyzed for their fit to Kuwait's unique environmental and socio-economic conditions. The AHP analysis highlights the paramount importance of "Health & Safety of Employees" (19.8%), "Water Pollution" (16.2%), and "Air Pollution Control" (15.4%) as criteria, underscoring a regional prioritization of health and environmental considerations over cost. Composting and RDF combustion emerge as the preferred disposal methods, with AHP-derived priority percentages of 22.7% and 22.3%, respectively. GA optimization further validates these methods as optimally aligned with the regional waste management framework. Conclusively, composting and RDF combustion are identified as the most feasible MSW disposal options for Kuwait, with potential applicability across the GCC, contingent on alignment with local economic and environmental policies. This research provides a critical foundation for policymakers and stakeholders in crafting sustainable, region-specific waste management strategies.

Identification of hazards for human health under chemical pollution in air inside in-patient hospitals

Use of various physical and chemical research techniques, including chromato-mass-spectrometry, made it possible to identify and quantify more than 40 organic compounds in air inside healthcare organizations, including saturated, unsaturated, cyclic, and aromatic hydrocarbons; terpenes, alcohols, aldehydes, esters, ketones, halogen-containing compounds, and organic acids. Levels of ethanol, dichloromethane, carbon tetrachloride, ethyl acetate, propyl acetate, acetone, terpene hydrocarbons, and acetic acid made the main contribution to the total content of all identified compounds. Most detected substances were present in concentrations not exceeding hygienic standards, except for chloroform and iodoform, the levels of which were up to 2 times higher than average daily MPL in intensive care wards and a bronchoscopy room. Organic acids and chlorinated organic compounds were found in elevated concentrations compared with insides of non-medical public buildings. Among the wide list of identified substances, hygienic standards have not been established for more than 70% of compounds and it is not possible to give a hygienic assessment of hazards or safety of their presence in air inside healthcare facilities. Despite that, the information obtained in this study is extremely useful for accomplishing an important stage in health risk analysis, which is identification of hazards for health of patients and healthcare workers posed by chemical air pollution inside healthcare organizations when using the risk analysis methodology. In this study, we assessed effects produced by operations of UV recirculator irradiators for air disinfection on its chemical composition inside healthcare institutions. The assessment showed that when such devices worked in the presence of patients and staff, there was an increase in the amount of pollutants in air and their total concentration grew from two to more than four times. When analyzing risks for health of staff and patients, hazard identification within risk-based control of chemical air pollution in the hospital environment should include monitoring of formaldehyde, styrene, ammonia, ethanol, isopropanol, chloroform, dichloroethane, acetic acid along with identification of a wide range of volatile organic compounds; it should also cover ammonia as one of the priority pollutants occurring in the environment from human excretory products.

Проблемы идентификации опасности для здоровья человека при химическом загрязнении воздушной среды помещений медицинских стационаров

Use of various physical and chemical research techniques, including chromato-mass-spectrometry, made it possible to identify and quantify more than 40 organic compounds in air inside healthcare organizations, including saturated, unsaturated, cyclic, and aromatic hydrocarbons; terpenes, alcohols, aldehydes, esters, ketones, halogen-containing compounds, and organic acids. Levels of ethanol, dichloromethane, carbon tetrachloride, ethyl acetate, propyl acetate, acetone, terpene hydrocarbons, and acetic acid made the main contribution to the total content of all identified compounds. Most detected substances were present in concentrations not exceeding hygienic standards, except for chloroform and iodoform, the levels of which were up to 2 times higher than average daily MPL in intensive care wards and a bronchoscopy room. Organic acids and chlorinated organic compounds were found in elevated concentrations compared with insides of non-medical public buildings. Among the wide list of identified substances, hygienic standards have not been established for more than 70% of compounds and it is not possible to give a hygienic assessment of hazards or safety of their presence in air inside healthcare facilities. Despite that, the information obtained in this study is extremely useful for accomplishing an important stage in health risk analysis, which is identification of hazards for health of patients and healthcare workers posed by chemical air pollution inside healthcare organizations when using the risk analysis methodology. In this study, we assessed effects produced by operations of UV recirculator irradiators for air disinfection on its chemical composition inside healthcare institutions. The assessment showed that when such devices worked in the presence of patients and staff, there was an increase in the amount of pollutants in air and their total concentration grew from two to more than four times. When analyzing risks for health of staff and patients, hazard identification within risk-based control of chemical air pollution in the hospital environment should include monitoring of formaldehyde, styrene, ammonia, ethanol, isopropanol, chloroform, dichloroethane, acetic acid along with identification of a wide range of volatile organic compounds; it should also cover ammonia as one of the priority pollutants occurring in the environment from human excretory products.

Health and economic cost estimates of short-term total and wildfire PM2.5 exposure on work loss: using the consecutive California Health Interview Survey (CHIS) data 2015–2018

InstructionTo help determine the health protectiveness of government regulations and policies for air pollutant control for Americans, our study aimed to investigate the health and economic impacts of work loss...

Greenery planning for urban air pollution control based on biomonitoring potential: Explicit emphasis on foliar accumulation of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs)

In this study, efficiencies of eight indigenous plants of Baishnabghata Patuli Township (BPT), southeast Kolkata, India, were explored as green barrier species and potentials of plant leaves were exploited for biomonitoring of particulate matter (PM) and polycyclic aromatic hydrocarbons (PAHs). The present work focused on studying PM capturing abilities (539.32−2766.27 μg cm−2) of plants (T. divaricata, N. oleander and B. acuminata being the most efficient species in retaining PM) along with the estimation of foliar contents of PM adhered to leaf surfaces (total sPM (large + coarse): 526.59−2731.76 μg cm−2) and embedded within waxes (total wPM (large + coarse): 8.73−34.51 μg cm−2). SEM imaging used to analyse leaf surfaces affirmed the presence of innate corrugated microstructures as main drivers for particle capture. Accumulation capacities of PAHs of vehicular origin (total index, TI > 4) were compared among the species based on measured concentrations (159.92−393.01 μg g−1) which indicated T. divaricata, P. alba and N. cadamba as highest PAHs accumulators. Specific leaf area (SLA) of plants (71.01−376.79 cm2 g−1), a measure of canopy−atmosphere interface, had great relevance in PAHs diffusion. Relative contribution (>90%) of 4–6 ring PAHs to total carcinogenic equivalent and potential as well as 5−6 ring PAHs to total mutagenic equivalent and potential had also been viewed with respect to benzo[a]pyrene. In−depth analysis of foliar traits and adoption of plant−based ranking strategies (air pollution tolerance index (APTI) and anticipated performance index (API)) provided a rationale for green belting. Each of the naturally selected plant species showed evidences of adaptations during abiotic stress to maximize survival and filtering effects for reductive elimination of ambient PM and PAHs, allowing holistic management of green spaces.

Outdoor Assessment of Airborne Microorganisms During the COVID-19 Outbreak in Madinah City, KSA

The COVID-19 outbreak was a serious challenge for countries around the globe. With the objective of mitigating the spread of the virus, both national and international health organizations swiftly enacted quarantine measures across numerous cities around the globe. This presented a unique chance to evaluate the consequences of human actions on the quality of the air. This study aimed to investigate airborne microbial levels in different outdoor locations in Al-Madinah City, Kingdom of Saudi Arabia (KSA), during the COVID-19 pandemic by comparing lockdown against non-lockdown conditions. Twelve outdoor locations were investigated in terms of microbial total counts using the open plate method during and after the COVID-19 lockdown. Environmental factors that could affect the airborne microbial load, including humidity, temperature and wind speed, were recorded during the assessment. The means of the total colony-forming units (CFU) for each cubic meter (m3) of air were calculated. Lockdown restrictions caused significant decreases in the biological contaminants in all locations compared with the numbers after the pandemic. Gram-positive bacteria represented most of the samples, with fewer fungal strains detected. The outdoor average total bacterial counts ranged between 0.00±0.00-8337.50±248.98 CFU/m3, compared with 2903.75±407.60-19722.50±475.03 CFU/m3 after the pandemic. The mean concentrations of total fungi were lower than those of bacteria and ranged between 0.00±0.00-143.75±131.75 CFU/m3 during the COVID-19 lockdown and were elevated after the lockdown to reach 28.75±49.80-776.25±298.78 CFU/m3. Based on the available data, there are no studies comparing outdoor microbial counts during and after the COVID-19 pandemic. Therefore, this research offers additional perspectives on the air quality experienced amidst the COVID-19 pandemic and the subsequent implementation of lockdown measures and could serve as a valuable resource for monitoring and implementing measures to control air pollution.

OMI-based emission source classification in East China and its spatial redistribution in view of pollution control measures

This study aims to generate a satellite-based qualitative emission source characterization for the heavily polluted eastern part of China in the 2010–2016 time period. The applied source identification technique relies on satellite-based NOx and SO2 emission estimates by OMI, their SO2:NOx ratio, and the MIX anthropogenic emission inventory to distinguish emissions from different emission categories (urban, industrial, natural) and characterize the dominant source per 0.25° × 0.25° grid cell in East China. Overall, we find good agreement between the satellite- and emission inventory–based spatiotemporal distribution and characterization of the dominant emission sources in East China in 2010–2016. In 2010, the satellite measurements suggest an emission distribution less dominated by industrial areas, a somewhat larger role for urban/transportation areas and agricultural activities, and more natural emissions in the southern part compared to the bottom-up emission categorization. In 2016, more than half of the classified emission categories over East China have remained the same. At the same time, there is a notable increase of agricultural lands and decrease of areas dominated by industry/transportation in 2016, suggestive of an overall decrease in heavy air pollution in East China over the course of 7 years. This is likely attributed to the sustained efforts of the Chinese government to drastically improve the air quality, especially since 2013 when the National Air Pollution Prevention and Control Action Plan was enacted. However, signs of urban expansion (urbanization) and rural–urban migration (“Go West” motion) stemmed from China’s rapid economic growth and labour demand are evident; escalating industrialization (even with cleaner means) and the urban population growth in East China resulted in stronger emissions from sources representing consumption and transportation which are strongly related to NO2 and PM10 pollution (rather than SO2) and are directly influenced by the population size. This resulted to a shift of the emissions from the east mainly to the north and northwest of East China. Overall, although the effectiveness of the Chinese environmental control policies has been successful, the air pollution problem remains an important concern.

Health assessment of emerging persistent organic pollutants (POPs) in PM2.5 in northern and central Taiwan

Over the last two decades, Taiwan has effectively diminished atmospheric concentrations of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs) through the adept utilization of advanced technologies and the implementation of air pollution control devices. Despite this success, there exists a dearth of data regarding the levels of other PM2.5-bound organic pollutants and their associated health risks. To address this gap, our study comprehensively investigates the spatial and seasonal variations, potential sources, and health risks of PCDD/Fs, Polychlorinated biphenyls (PCBs), and Polychlorinated naphthalene (PCNs) in Northern and Central Taiwan. Sampling collections were conducted at three specific locations, including six municipal waste incinerators in Northern Taiwan, as well as a traffic and an industrial site in Central Taiwan. As a result, the highest mean values of PM2.5 (20.3–39.6 μg/m3) were observed at traffic sites, followed by industrial sites (14.4–39.3 μg/m3), and the vicinity of the municipal waste incinerator (12.4–29.4 μg/m3). Additionally, PCDD/Fs and PCBs exhibited discernible seasonal fluctuations, displaying higher concentrations in winter (7.53–11.9 and 0.09–0.12 fg I-TEQWHO/m3) and spring (7.02–13.7 and 0.11-0.16 fg I-TEQWHO/m3) compared to summer and autumn. Conversely, PCNs displayed no significant seasonal variations, with peak values observed in winter (0.05–0.10 fg I-TEQWHO/m3) and spring (0.03–0.08 fg I-TEQWHO/m3). Utilizing a Positive Matrix Factorization (PMF) model, sintering plants emerged as the predominant contributors to PCDD/Fs, constituting 77.9% of emissions. Woodchip boilers (68.3%) and municipal waste incinerators (21.0%) were identified as primary contributors to PCBs, while municipal waste incinerators (64.6%) along with a secondary copper and a copper sludge smelter (22.1%) were the principal sources of PCNs. Moreover, the study specified that individuals aged 19–70 in Northern Taiwan and those under the age of 12 years in Central Taiwan were found to have a significantly higher cancer risk, with values ranging from 9.26 x 10−9-1.12 x 10−7 and from 2.50 x 10−8-2.08 x 10−7respectively.

Unraveling the influence of biogenic volatile organic compounds and their constituents on ozone and SOA formation within the Yellow River Basin, China

Biogenic volatile organic compounds (BVOC) assume a pivotal role during the formation stages of ozone (O3) and secondary organic aerosols (SOA), serving as their primary precursors. We used the latest MEGAN3.1 model, updated vegetation data and emission factors, combined with MODIS data analysis to simulate and estimate the integrated emissions of BVOC from nine provinces in China's Yellow River Basin in 2018. Following an extensive evaluation of the WRF-CMAQ model utilizing diverse parameters, the simulated and observed values had correlation coefficients between them that ranged from 0.94 to 0.99, implying a favorable outcome in terms of simulation efficacy. The findings from the simulation analysis reveal that the combined BVOC emissions from the nine provinces in the Yellow River Basin reached a total of 6.51 Tg in 2018. Among these provinces, Sichuan, Henan, and Shaanxi ranked highest, with emissions of 1.28 Tg, 1.04 Tg, and 0.96 Tg, respectively. BVOC emissions led to concentrations of 36.72 μg/m³ in the daily maximum 8-h ozone and 0.59 μg/m³ in the average SOA in nine provinces of the Yellow River Basin in July. Isoprene contributed the most to the O3 production with 6.31 μg/m3, and monoterpenes contributed the most to SOA production with 0.45 μg/m3. ΔSOA and ΔOzone are mainly distributed in the belts of central Sichuan Province, southern Shaanxi Province, western Henan Province, northern Qinghai Province, central Inner Mongolia, and southern Shanxi Province, and most of these areas are located 50 km around the Yellow River. O3 and SOA in Taiyuan, Xi'an, Chengdu, and Zhengzhou cities are strongly influenced by the generation of BVOCs. This study provides a reliable scientific basis for the prevention and control of air pollution in the Yellow River Basin.

Impact of comprehensive air pollution control policies on six criteria air pollutants and acute myocardial infarction morbidity, Weifang, China: A quasi-experimental study

Comprehensive air pollution control policies may reduce pollutant emissions. However, the impact on disease morbidity of the change for the concentration of air pollutants following the policies has been insufficiently studied. We aim to assess the impact of comprehensive air pollution control policies on the levels of six criteria air pollutants and acute myocardial infarction (AMI) morbidity in Weifang, China. This study performed an interrupted time series analysis. The linear model with spline terms and generalized additive quasi-Poisson model were used to estimate the immediate change from 2016 to 2019 in the daily concentration of six air pollutants (PM2.5, PM10, SO2, NO2, O3, and, CO) and AMI incident cases (Age ≥35) associated with the implementation of air pollution control policies in Weifang, respectively. After the implementation of air pollution control policies, air quality in Weifang had been improved. Specifically, the daily concentrations of PM2.5, PM10, SO2, and, CO immediately decreased by 27.9 % (95 % CI: 6.6 % to 44.3 %), 32.9 % (95 % CI: 17.5 % to 45.5 %), 14.6 % (95 % CI: 0.4 % to 26.8 %), and 33.9 % (95 % CI: 22.0 % to 44.0 %), respectively. In addition, the policies implementation was also associate with the immediate decline in the AMI morbidity (−6.5 %, 95 % CI: −10.4 % to −2.3 %). And subgroup analyses indicate that the health effects of the policy intervention were only observed in female (−9.4 %, 95 % CI: −14.4 % to −4.2 %) and those aged ≥65 years (−10.5 %, 95 % CI: −14.6 % to −6.2 %). During the final 20 months of the study period, the policy intervention was estimated to prevent 1603 (95 % CI: 574 to 2587) cases of incident AMI in Weifang. Our results provide strong rationale that the policy intervention significantly reduced ambient pollutant concentrations and AMI morbidity, which highlighted the importance for a comprehensive and rigorous air pollution control policy in regions with severe air pollution.

Air pollutant emissions caused by receiving international industrial transfer in Southeast Asian developing countries from 1990 to 2018

Receiving international industrial transfer (mainly foreign direct investment, FDI) is extremely important for economic development but also brings negative environmental impacts for Southeast Asian developing countries (SEADCs). Due to relatively low labor costs and large market potential, SEADCs have become an attractive destination for industrial transfer after China, while studies were far from sufficient on the associated air pollutant emissions that would worsen air quality and threaten human health. We develop an exploratory framework to estimate the long-term trends of relevant air pollutant emissions in eight major SEADCs, including Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Thailand, and Vietnam. During 1990–2018, the emissions generally show a fluctuating upward trend and increased significantly in Cambodia, Laos, Philippines, and Vietnam. The total emissions of CO, NMVOC, SO2, NOX, PM2.5, and NH3 from the eight SEADCs increased from 19.0, 4.3, 3.6, 1.5, 0.5, and 0.4 kilotons (kt) to 391.6, 260.9, 271.1, 182.4, 48.4, and 12.2 kt, respectively. The emission growth in almost all SEADCs accelerated after 2008 and faster than FDI growth. The disparities in emissions among SEADCs basically grew first and then declined to a level lower than that of 1990, but generally exceeded the disparities in FDI. Productivity gain and emission intensity decrease primarily caused the emission growth and reduction, respectively. Relatively small reductions in emission intensity are found for NOX and SO2. In general, most SEADCs have utilized FDI for economic development without sufficient efforts on air pollutant emission controls. Our outcomes can inform the formulation and optimization of relevant policies reconciling economic development and air quality improvement in SEADCs.

An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction

In recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial–temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM2.5, PM10 and O3 at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.

Spatial–temporal distribution patterns and influencing factors analysis of comorbidity prevalence of chronic diseases among middle-aged and elderly people in China: focusing on exposure to ambient fine particulate matter (PM2.5)

ObjectiveThis study describes regional differences and dynamic changes in the prevalence of comorbidities among middle-aged and elderly people with chronic diseases (PCMC) in China from 2011–2018, and explores distribution patterns and the relationship between PM2.5 and PCMC, aiming to provide data support for regional prevention and control measures for chronic disease comorbidities in China.MethodsThis study utilized CHARLS follow-up data for ≥ 45-year-old individuals from 2011, 2013, 2015, and 2018 as research subjects. Missing values were filled using the random forest machine learning method. PCMC spatial clustering investigated using spatial autocorrelation methods. The relationship between macro factors and PCMC was examined using Geographically and Temporally Weighted Regression, Ordinary Linear Regression, and Geographically Weighted Regression.ResultsPCMC in China showing a decreasing trend. Hotspots of PCMC appeared mainly in western and northern provinces, while cold spots were in southeastern coastal provinces. PM2.5 content was a risk factor for PCMC, the range of influence expanded from the southeastern coastal areas to inland areas, and the magnitude of influence decreased from the southeastern coastal areas to inland areas.ConclusionPM2.5 content, as a risk factor, should be given special attention, taking into account regional factors. In the future, policy-makers should develop stricter air pollution control policies based on different regional economic, demographic, and geographic factors, while promoting public education, increasing public transportation, and urban green coverage.

The Behavior and Removal of Condensable Particulate Matter in Flue Gas in a Multi-Field Force: A Modeling and Experimental Study

Condensable particulate matter (CPM) is different from filterable particulate matter (FPM), which could escape from air pollution control devices (APCDs) and pose a great threat to the environment and human health. Thus, modeling and experimental studies were conducted on CPM particle behavior and removal, in a cold electrode electrostatic precipitator (CE−ESP) coupled with a electric field, temperature field and concentration field. A multi−field force coupling model was then established that was based on the mechanical behavior of particles inside the CE−ESP. The results showed that temperature field was beneficial to depositing small size particles and that, the greater the temperature gradient, the higher CPM’s removal efficiency. While the electric field tended to gather larger size particles, the greater the voltage provided, the higher the removal efficiency for CPM and FPM. In the multi−field, the augmented coagulation and the removal efficiencies of both CPM and FPM increased significantly, reaching 89% and 98%, respectively. Subsequently, experiments were conducted by a self−made CE−ESP device, which showed the removal efficiencies of CE−ESP of a CPM in a multi−field were 91% and 81% for a coal−fired power plant and a waste incineration plant, respectively. This research could make a great contribution to CPM condensation, aggregation and removal.

Advanced Filtration Techniques in Environmental Engineering

Granular filters and membrane filters, two essential filtration methods in environmental engineering, are thoroughly examined in this study. These techniques greatly aid air pollution control problems and water and wastewater treatment. The first section of the study outlines the basic ideas that underpin each filtration technique. The mechanical particle trapping mechanisms of granular filters, their wide range of applications in water treatment, and design factors, including media kinds and layers, are all examined. On the other hand, membrane filters are closely scrutinised because of their size exclusion principles, variety of types (from microfiltration to ultrafiltration), and vital function in industrial waste treatment and desalination processes. The study goes into additional detail about the maintenance, clogging, and financial ramifications that different filtering techniques must deal with. A comparison analysis provides insights into each method's applicability for various environmental engineering applications by illuminating its efficacy, affordability, and application specificity. The study's conclusion considers the potential for growth and current and upcoming technical developments in the filtration industry. This study lays the groundwork for forthcoming advancements and uses in the field of environmental engineering while also highlighting the significance of these filtering methods in modern ecological engineering.

Ambient particulate matter and surrounding greenness in relation to sleep quality among pregnant women: A nationwide cohort study

BackgroundParticulate air pollution and residential greenness are associated with sleep quality in the general population; however, their influence on maternal sleep quality during pregnancy has not been assessed. ObjectiveThis cross-sectional study investigated the individual and interactive effects of exposure to particulate matter (PM) air pollution and residential greenness on sleep quality in pregnant women. MethodsPregnant women (n = 4933) enrolled in the Korean Children's Environmental Health Study with sleep quality information and residential address were included. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). The average concentrations of PM (PM2.5 and PM10) during pregnancy were estimated through land use regression, and residential greenness in a 1000 m buffer area around participants' residences was estimated using the Normalized Difference Vegetation Index (NDVI1000-m). Modified Poisson regression models were used to estimate the associations between PM and NDVI and poor sleep quality (PSQI >5) after controlling for a range of covariates. A four-way mediation analysis was conducted to examine the mediating effects of PM. ResultsAfter adjusting for confounders, each 10 μg/m3 increase in PM2.5 and PM10 exposure was associated with a higher risk of poor sleep quality (relative risk [RR]: 1.06; 95% confidence interval [CI]: 1.01, 1.11; and RR: 1.09; 95% CI: 1.06, 1.13, respectively), and each 0.1-unit increase in NDVI1000-m was associated with a lower risk of poor sleep quality (RR: 0.97; 95% CI: 0.95, 0.99). Mediation analysis showed that PM mediated approximately 37%–56% of the association between residential greenness and poor sleep quality. ConclusionsThis study identified a positive association between residential greenness and sleep quality. Furthermore, these associations are mediated by a reduction in exposure to particulate air pollution and highlight the link between green areas, air pollution control, and human health.

Investigation of optimal utilization of solar energy in electric vehicles: An economical and low carbon growth perspectives

Solar energy is an essential source of clean, renewable energy that has the potential to transform our energy system and reduce impact on the environment. But, only 3.6% of solar energy is effectively used around the globe for electricity generation. So, using solar energy to the maximum extent is essential to control air pollution and save the environment for future generations. This article discusses the operative use of solar energy and suggests measures for optimal solar energy utilization in electric vehicles (EVs). The water-based silicon dioxide (SiO2) and aluminum oxide (Al2O3) combination is considered due to the high refractive index of SiO2 and scratch resistance and durability of Al2O3. A theoretical model was established by considering the shape of the solar collector, temperature change, nano spray, and radiative heat. The modeled problem was computationally resolved using the bvp5c MATLAB scheme, and simultaneous results were explored for various outlines of solar collectors. It is found that the shape of the solar collector and nanospray/nanocoating play a significant role in the practical usage of solar energy. Using curved-shaped solar collectors with water-SiO2-Al2O3 nanocoating allows the solar collector to absorb more light. This helps to improve the effectiveness of solar collectors in running EVs, which can reduce carbon growth.