Contribution To Pollution Research Articles

Remote Sensing Technologies Quantify the Contribution of Ambient Air Pollution to Asthma Severity and Risk Factors in Greenness, Air Pollution, and Wildfire Ecological Settings: A Literature Review

Numerous epidemiologic studies have used remote sensing to quantify the contribution of greenness, air pollution, and wildfire smoke to asthma and other respiration outcomes. This is the first review paper to evaluate the influence of remote sensing exposures on specific outcome severity and risk factors in different ecological settings. Literature searches utilizing PubMed and Google Scholar identified 61 unique studies published between 2009 and 2023, with 198 specific outcomes. Respiration-specific outcomes were lower in greenness and higher in air pollution and wildfire ecological settings. Aerosol optical depth (AOD)-PM2.5 readings and specific outcomes were higher in economically developing than in economically developed countries. Prospective studies found prenatal and infant exposure to higher ambient AOD-PM2.5 concentration level readings contributed to higher childhood asthma incidence. Lung function was higher in greenness and lower in the other two ecological settings. Age, environment, gender, other, and total risk factors showed significant differences between health outcomes and ecological settings. Published studies utilized physiologic mechanisms of immune, inflammation, and oxidative stress to describe obtained results. Individual and total physiologic mechanisms differed between ecological settings. Study results were used to develop a descriptive physiologic asthma model and propose updated population-based asthma intervention program guidelines.

Air quality monitoring and assessment based on AIS data

Abstract Indonesia is an archipelagic country, where sea transportation is highly needed to connect the islands among Indonesian regions. Indonesian waters are also passed by international shipping passing through the Indonesia Archipelagic Sea Lane. Number of ships tends to increase by increasing the economic activities in Indonesia. The increasing number of ships will potentially cause the increase of air pollution in Indonesia waters. AIS consortium has been established since 2020 focusing on conducting join research and development on several issues that can be solved using Automatic Identification System (AIS) data gathered from AIS devices that have been installed in several universities as members of the consortium. One potential use of AIS data is the estimation of capacity and spreading of air pollution caused by shipping activities in Indonesia waters. As AIS can provide ship’s navigation status time-to-time, the estimation of fuel consumption as well as the emission can be estimated in real time. For highly accusation fuel oil consumption prediction is done by using the Random Forest Model and ship emissions including CO2, NO2, SO2, PM2.5, and PM10 are estimated based on emission factors from ships. This analysis is mapping the contribution of air pollution based on ship type in the Tanjung Perak port area. Later, it is expected to become the basis for Indonesia’s maritime operation policy in order to monitor and control the emission contributed by the ships in Indonesian waters.

Assessment of long-term exposure to traffic-related air pollution: An exposure framework.

Exposure to ambient air pollution is associated with morbidity and mortality, making it an important public health concern. Emissions from motorized traffic are a common source of air pollution but evaluating the contribution of traffic-related air pollution (TRAP) emissions to health risks is challenging because it is difficult to disentangle the contribution of individual air pollution sources to exposure contrasts in an epidemiological study. This paper describes a new framework to identify whether air pollution differences reflect contrasts in TRAP exposures. Because no commonly measured pollutant is entirely specific to on-road motor vehicles, this exposure framework combined information on pollutants, spatial scale (i.e., geographic extent), and exposure assessment methods and their spatial scale to determine whether the estimated effect of air pollution in a given study was related to differences in TRAP. The exposure framework extended beyond the near-road environment to include differences in exposure to TRAP at neighborhood resolution ( ≤ 5 km) across urban, regional, and national scales. It also embedded a stricter set of criteria to identify studies that provided the strongest evidence that exposure contrasts were related to differences in traffic emissions. Application of the framework to the transparent selection of epidemiological studies for a systematic review produced insights on assessing and improving comparability of TRAP exposure measures, particularly for indirect measures such as distances from roads. It also highlighted study design challenges related to the duration of measurements and the structure of epidemiological models. This manuscript describes a new exposure framework to identify studies of traffic-related air pollution, a case study of its application in an HEI systematic review, and its implications for exposure science and air pollution epidemiology experts. It identifies challenges and provides recommendations for the field going forward. It is important to bring this information to the attention of researchers in air pollution exposure science and epidemiology because applying the broader lessons learned will improve the conduct and reporting of studies going forward.

Pedestrian traffic is the main driver of macro- and large microplastic debris deposition in urban stormwater drains

Land-based sources are the greatest contributors of plastic pollution found in aquatic environments. Although plastic debris items spilling into natural watercourses from stormwater outflow sites have been investigated, this study provides details of the plastic items that can be trapped within stormwater drains prior to release. We examined macroplastic (>5 mm) and large microplastic (1–5 mm) debris that accumulated in LittaTrapTM devices at six drains over four seasonal periods in London, Ontario, Canada. Flotation, visual identification, microscopy and Fourier transform infrared spectroscopy (FTIR) were used to determine the drivers of plastic debris deposition. Macroplastics (MaPs) and microplastics (MPs) were identified in all 36 samples, and the totals ranged from 5–158 MaPs and 18–359 MPs per trap. Out of the 118 different MaPs found, the most common items were cigarette butts, wrappers, and expanded polystyrene. The main MPs were fragments, foams, and fibres. The most common macroplastic applications were “smoking”, “food/beverage packaging”, “household”, and non-food or beverage “packaging”. Microplastic particle compositions were mainly polyethylene and polypropylene, but other polymer types fall within the applications of construction (paints and resins), automotive/transportation, and electronics. The summer samples contained the greatest averages of plastic debris, and the drains located in busy pedestrian areas were associated with the highest debris counts. The results support pedestrian traffic as the main driver of plastic debris accumulation in urban London stormwater drains, which is controlled by seasonal weather conditions.

Antibiotic resistance evolution driven synergistically by antibiotics and typical organic pollutants in antibiotic production wastewater

A major concern regarding the risk of antibiotic production wastewater (APW) for the transmission of antibiotic resistance (AR) stems from the residual antibiotics. However, APW also contains high concentrations of organic pollutants, many of which have severe biological toxicity and joint toxicity with antibiotics. The contribution of these organic pollutants to the development of AR in the APW treatment system is unknown. In this study, a wild-type Escherichia coli strain was exposed to six typical organic pollutants in APW individually and synergistically with the antibiotic ampicillin (AMP). Independent exposure to organic compounds had negligible effects on the evolution of AR, whereas they synergistically induced AR mutations and increased antibiotic persistence with AMP, especially the raw material d-p-hydroxyphenylglycine (DHPG), at relevant concentrations in APW. Combined exposure to 1–500 mg/L DHPG and 1 mg/L AMP synergistically increased the mutation frequencies against multiple antibiotics by up to 2928.9-fold in a dose–time pattern, and the combination index reached 445.7. Phenotypic and genotypic analyses revealed that the synergism between DHPG and AMP was associated with increased antibacterial activity, enhanced oxidative stress, and stimulation of efflux pump expression. Overall, our results highlight the elevated risk of AR induction caused by antibiotics and organic pollutants in APW.

Application of time series and multivariate statistical models for water quality assessment and pollution source apportionment in an Urban River, New Jersey, USA

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources.Graphical

An Assessment of Water Quality and Pollution Sources in a Source Region of Northwest China

China prioritizes ensuring drinking water safety, particularly in the water-scarce northwest region. This study, utilizing water quality data from 52 village and town water sources since August 2022, assesses water quality, with a specific focus on key indicators related to organic pollution sources. This study provides a scientific foundation for enhancing water quality in these sources. Employing category factor analysis for classification and grading, principal component analysis for qualitative analysis of key evaluation indicators, and the absolute principal component linear regression equation for quantitative calculation of pollution sources, this study reveals that all 52 water sources meet quality standards. Principal component analysis categorizes pollution sources as diverse types of organic compounds in surface water. Source analysis calculations highlight decay-type organic substances as major contributors to increased water color and permanganate index, with pollution contribution rates of 54.78% and 31.31%, respectively. Fecal-type organic substances dominate the increase in dissolved total solids and total coliforms, with pollution contribution rates of 56.65% and 40.16%, respectively. Additionally, high-molecular-weight organic substances exhibit lower concentrations in the water. This article presents a systematic water quality assessment methodology, which is used for the first time to qualitatively assess the types of water sources and to quantitatively trace specific sources of organic pollution in source water in northwest China. This systematic study’s results, involving initial assessment followed by traceability, recommend the adoption of a simple contact filtration and disinfection process to enhance water quality in the region.

Age trend in the mortality from diseases of the circulatory system during the pandemic under a decrease in air pollution

Introduction. The results of large-scale studies of the mortality from diseases of the circulatory system (DCS) during the pandemic require further analysis of the data and the search for modifying factors. The purpose is to identify the features of the trend in the mortality from DCS in the population of an industrial center during a pandemic under changes in air pollution. Materials and methods. The research was carried out in the industrial center – Bratsk. Age-specific mortality rates were studied using exponential models in the background (2017) and pandemic (2021) periods. The contribution of atmospheric air pollution to the mortality rate was calculated in accordance with the “Guidelines for assessing the risk to public health from exposure to chemical substances that pollute the environment.” Results. The age-related increase in the mortality rate from DCS over 2017 was 125%, and during 2021 – 172%. During the pandemic, the excess mortality rate was in cases older 80 years – 35.92‰, 70–79 – 8.48‰, 60–69 – 1.03‰. PM10 levels in the air decreased from high to alarming levels in 2021, resulting in a reduction in excess PM10-related deaths from 194 (CI: 193.6–195.1) to 5.0 (CI: 4.8–5.1) cases. Limitations are associated with incomplete epidemiological knowledge about the dependence of mortality from DCS on exposure to air pollutants. Conclusion. The use of nonlinear regression analysis made it possible to demonstrate changes in trends in age-specific mortality during the background period and during the pandemic. During the pandemic, the level of excess mortality was revealed to relate with PM10 air pollution decreased.

Water quality benefits of implementing Green and Blue Infrastructure in a peri-urban catchment – Case study of a Brazilian metropolis

This paper investigates the effectiveness of Green and Blue Infrastructure (GBI) in managing non-point source pollution in the 120 km2 catchment of the Vargem das Flores reservoir, which supplies water to 600,000 inhabitants in the Belo Horizonte metropolitan area, Brazil's third-largest metropolis. A calibrated water quality model within the Storm Water Management Model (SWMM) was used to simulate total suspended solids (TSS) and nutrients loads under different future land use scenarios. The first scenario reflects maximum urban development according to current legislation. An alternative scenario was proposed considering the protection of forest remnants and the adoption of the most cost-effective GBI techniques in newly developed areas. Results indicated a substantial contribution of diffuse pollution to the reservoir's water quality degradation, mainly due to TSS loads transported from the most urbanized subcatchments. The application of bioretention cells to treat 12.6 km2 new impervious areas effectively mitigated diffuse pollution, removing 52.1% of TSS, 19.5% of total phosphorus (TP), and 17.1% of chemical oxygen demand (COD) loads flowing into the reservoir. The GBI implementation would maintain pollutant loads similar to current levels despite the increased urbanization.

Responses of surface ozone under the tropical cyclone circulations: Case studies from Fujian Province, China

The circulation of tropical cyclones (TCs) exerts a multifaceted influence on the spatial and temporal distribution of surface pollutants. This study investigates the response of surface ozone (O3) concentration to the TCs in Fujian Province from June to December 2022 by analyzing the contributions of atmospheric pollutants, meteorological conditions, and dynamical transports. Empirical orthogonal function (EOF) decomposition methods are used to analyze the spatio-temporal distribution patterns of affected O3, and a Gradient Boosting Regression Trees (GBRT) machine learning model is employed to estimate surface O3 concentration, quantifying the influence of each factor. The results indicate an anomaly increase in O3 concentration during this period, with photochemistry-related meteorological conditions being the primary influencer, accounting for 66.9% of O3 variations, elucidating the interpretability of the GBRT model for attributing changes in O3 concentration. Low relative humidity and high temperature conditions have been identified as pivotal factors influencing the rise in O3 concentrations. The presence of TC undermines this predominant influence, amplifying the role of transport factors and other atmospheric pollutants. In the case studies of TC (Muifa and Nanmadol, 2022), the slow or stagnant TCs triggered persistent downdrafts in its periphery and brought favorable meteorological conditions such as clear sky and warm temperature for photochemistry. TCs also enhances the impact of horizontal and vertical dynamic transport on O3 concentrations. This work provides vital insights into the complex interplay between TCs and surface O3 concentrations, highlighting the need for targeted environmental and air quality management strategies in regions frequently impacted by TCs.

Microglia Mediate Metabolic Dysfunction From Common Air Pollutants Through NF-κB Signaling.

The prevalence of Type 2 Diabetes (T2D) poses a significant health challenge yet the contribution of air pollutants to T2D epidemics remains understudied. Several studies demonstrated a correlation between exposure to volatile organic compounds (VOCs) in indoor/outdoor environments, and T2D. Here, we conducted the first meta-analysis, establishing a robust association between exposure to benzene, a prevalent airborne VOC, and insulin resistance in humans across all ages. We utilized a controlled benzene exposure system, continuous glucose monitoring (CGM) approach and indirect calorimetry in mice, to investigate the underlying mechanisms. Following exposure, disruptions in energy homeostasis, accompanied by modifications in the hypothalamic transcriptome and alterations in insulin and immune signaling, were observed exclusively in males, leading to a surge in blood glucose levels. In agreement, RNA-sequencing of microglia reveals increased expression of genes associated with immune response and NF-κB signaling. Selective ablation of IKKβ in immune cells (Cx3cr1GFPΔIKK) or exclusively in microglia (Tmem119ERΔIKK) in adult mice alleviated benzene-induced gliosis, restored energy homeostasis, hypothalamic gene expression, and protected against hyperglycemia. We conclude that the microglial NF-κB pathway plays a critical role in chemical-induced metabolic disturbances, revealing a vital pathophysiological mechanism linking exposure to airborne toxicants and the onset of metabolic diseases.

Driving factor, source identification, and health risk of PFAS contamination in groundwater based on the self-organizing map

The complex interactions between groundwater chemical environments and PFAS present challenges for data analysis and factor assessment of the spatial distribution and source attribution of PFAS in groundwater. This study employed spatial response analysis combining self-organizing maps (SOM), K-means clustering, Spearman correlation, positive matrix factorization (PMF) and risk quotient (RQ), to uncover the spatial characteristics, driving factors, sources, and human health risks of groundwater PFAS in the Pearl River Basin. The results indicated that the characteristics of PFAS in groundwater were classified into 16 neurons, which were further divided into 6 clusters (I—VI). This division was due to the contribution of industrial pollution (33.2 %) and domestic pollution (31.5 %) to the composition of PFAS in groundwater. In addition, the hydrochemical indicators such as pH, dissolved organic carbon (DOC), chloride (Cl−), and calcium ions (Ca2+) might also affect the distribution pattern of PFAS. The potential human health risk in the area was minimal, with cluster Ⅱ presenting the highest risk (RQ value 0.25) which is closely related to PFOA emissions from fluoropolymer industry. This study provides a theoretical basis and data support for applying of SOM to the visualization and control of PFAS contamination in groundwater.

Trajectories of long-term exposure to PCB153 and Benzo[a]pyrene (BaP) air pollution and risk of breast cancer

BackgroundWhile genetic, hormonal, and lifestyle factors partially elucidate the incidence of breast cancer, emerging research has underscored the potential contribution of air pollution. Polychlorinated biphenyls (PCBs) and benzo[a]pyrene (BaP) are of particular concern due to endocrine-disrupting properties and their carcinogenetic effect.ObjectiveTo identify distinct long term trajectories of exposure to PCB153 and BaP, and estimate their associations with breast cancer risk.MethodsWe used data from the XENAIR case–control study, nested within the ongoing prospective French E3N cohort which enrolled 98,995 women aged 40–65 years in 1990–1991. Cases were incident cases of primary invasive breast cancer diagnosed from cohort entry to 2011. Controls were randomly selected by incidence density sampling, and individually matched to cases on delay since cohort entry, and date, age, department of residence, and menopausal status at cohort entry. Annual mean outdoor PCB153 and BaP concentrations at residential addresses from 1990 to 2011 were estimated using the CHIMERE chemistry-transport model. Latent class mixed models were used to identify profiles of exposure trajectories from cohort entry to the index date, and conditional logistic regression to estimate their association with the odds of breast cancer.Results5058 cases and 5059 controls contributed to the analysis. Five profiles of trajectories of PCB153 exposure were identified. The class with the highest PCB153 concentrations had a 69% increased odds of breast cancer compared to the class with the lowest concentrations (95% CI 1.08, 2.64), after adjustment for education and matching factors. The association between identified BaP trajectories and breast cancer was weaker and suffered from large CI.ConclusionsOur results support an association between long term exposure to PCB153 and the risk of breast cancer, and encourage further studies to account for lifetime exposure to persistent organic pollutants.

Multifaceted contribution of environmental pollution, race and income to health inequities in Texas

Abstract Prior studies found links between ethnic background, socioeconomic status, and proximity to toxic environmental contaminants harmful to human health. However, there is no consensus among environmental economists on whether ethnicity or economics are the primary causes of health inequity under the influence of environmental hazard exposure. This paper explores this research question in Texas, the second largest US state, and the most diverse demographically, using a comprehensive framework with twelve main factors as key determinants for environmental-related health outcomes. The matrix of associations among factors of environmental pollution, economic class, race/ethnicity, and state of health is very complicated by multiple inter-correlations among components. To differentiate the relative importance of various factors, twelve statistically large population cohorts were compared, based on four racial/ethnic groups, each with three different levels of poverty. This novel approach allows for more meaningful comparisons, by normalizing groups for ethnicity and prevalence of poverty, two of the most influential socioeconomic factors. Compared to majority-White communities, majority-Hispanic and -Black communities were found to be more disproportionally negatively impacted by environmental pollution and socioeconomic challenges. This resulted in worse health outcomes: higher prevalence of chronic diseases and a shortened life span. The prevalence of poverty appears to play a dominant role in health outcomes across all racial/ethnic groups. Consistent with prior research, the Hispanic community has shown a strong positive correlation with the prevalence of diabetes, while the Black community has a high prevalence of asthma.

A new hybrid deep neural network for multiple sites PM2.5 forecasting

Many studies have confirmed that fine particulate matter (PM2.5) poses significant hazards to both human health and the ecological environment. Predicting future trends in PM2.5 concentrations is crucial for preventing air pollution hazards to human health. However, current machine learning-based forecasting models often focus on specific regions or sites, resulting in poor spatial generalization performance. To address this issue, this study utilizes ERA5 meteorological reanalysis data, MERRA2 assimilated aerosol optical depth (AOD) data, and ground pollutant monitoring site data to construct a three-dimensional dataset. A novel hybrid deep neural network model (D_GAT) is proposed to simultaneously forecast the future 72h PM2.5 concentration trends for all sites in China. The D_GAT model combines the advantages of a graph attention network and a long short-term memory neural network, which can effectively aggregate spatial information between sites and simulate the spatial transport process and time series change characteristics of pollutant particles. Moreover, the proposed calculation method for attention scores quantified based on the actual pollutant propagation characteristics, combined with the upper atmosphere pollution transport process represented by AOD, can better express the pollution contributions of different neighboring sites to the predicted central site. Experimental results show that, compared to four other baseline models, the novel model achieves an average R2 of 0.732 for all sites in 72-h prediction, which is the highest among the five models, and the prediction accuracy of 72h is also in the forefront compared with the current similar research. The RMSE and MAE are 14.63 μg/m3 and 9.49 μg/m3, respectively, which are 2–4 μg/m3 lower than the other four baseline models, indicating that it has higher reliability and can achieve the goal of timely prediction and early warning. Overall, the new model is an effective tool for multiple sites generalization forecasting.

Traffic assignment optimization to improve urban air quality with the unified finite-volume physics-informed neural network

Emissions by road transportation constitute the major contributor of air pollutants to threat the public health, especially on mega cities with high-rise buildings and growing population. A practicable and economical remedy is assigning the traffic volume judiciously, termed traffic assignment optimization (TAO). Incorporating the computational fluid dynamics (CFD), this method provides precisely optimized traffic volumes under versatile meteorological conditions. However, the high-dimensional degree of freedoms (DoF) involved in CFD hampers its further application toward the large-scale problem where a massive urban area is considered. With the more complicated urban traffic network, the larger decision variable dimension also impedes the time-efficient acquisition of the optimization results. To alleviate this curse-of-dimensionality, the current work proposes an optimization framework with the surrogate model based on the unified finite-volume physics-informed neural networks (UFV-PINN). The UFV-PINN plays the dual role as the partial differential equation (PDE) solver and the surrogate model for pollutant concentration prediction. It reduces the DoF within the PDE solution process and enables the gradient-based optimization. The current work optimizes the average CO concentration and accumulative travel time in Kowloon peninsula of Hong Kong, using the multiple-gradient descent algorithm (MGDA). This optimization framework is tested with various working conditions. Results indicate that the proposed method attain high accuracy solutions, comparable to the heuristic algorithms. This study is the first attempt to incorporate UFV-PINN into the TAO with air quality consideration. Endowed with the differentiability by UFV-PINN, the framework will facilitate the TAO to provide precise suggestions toward large-scale problems.

Masks As a New Hotspot for Antibiotic Resistance Gene Spread: Reveal the Contribution of Atmospheric Pollutants and Potential Risks.

The consumption of disposable surgical masks (DSMs) considerably increased during the coronavirus pandemic in 2019. Herein, we explored the spread of antibiotic resistance genes (ARGs) and the potential risks of antibiotic resistant bacteria (ARB) on DSMs. At environmentally relevant concentrations, the conjugate transfer frequency (CTF) of ARGs increased by 1.34-2.37 folds by 20 μg/m3 of atmospheric water-soluble inorganic ions (WSIIs), and it increased by 2.62-2.86 folds by 80 ng/m3 of polycyclic aromatic hydrocarbons (PAHs). Total suspended particulates (TSP) further promoted the CTF in combination with WSIIs or PAHs. Under WSII and PAH exposure, gene expression levels related to oxidative stress, cell membrane, and the adenosine triphosphate (ATP) were upregulated. WSIIs predominantly induced cellular contact, while PAHs triggered ATP formation and membrane damage. Molecular dynamics simulations showed that WSIIs and PAHs reduced membrane lipid fluidity and increased membrane permeability through interactions with the phosphatidylcholine bilayer. DSM filtering performance decreased, and the CTF of ARGs increased with the wearing time. The gut simulator test showed that ARB disrupted the human gut microbial community and increased total ARG abundance but did not change the ARG abundance carried by ARB themselves. A mathematical model showed that long-term WSII and PAH exposure accelerated ARG dissemination in DSMs.

A deep learning-based model for estimating pollution fluxes from rivers into the sea and its optimization

Pollution fluxes from rivers into the sea are currently the main source of pollutants in nearshore areas. Based on the source-sink process of the basin-estuary-coastal waters system, the pollution fluxes into the sea and their spatiotemporal heterogeneity were estimated. A deep learning-based model was established to simplify the estimation of pollution fluxes into the sea, with socio-economic drivers and meteorological data as input variables. A method for estimating the contribution rate of pollution fluxes from different spatial gradient was proposed. In this study, we found that (1) the pollution fluxes into the sea of total nitrogen (TN) and total phosphorus (TP) from the Bohai Sea Rim Basin (BSRB) in 1980, 1990, 2000, 2010, and 2020 were 25.38 × 104, 26.12 × 104, 27.27 × 104, 29.82 × 104, 25.31 × 104 and 1.32 × 104, 2.14 × 104, 2.09 × 104, 1.87 × 104, 1.68 × 104 tons, respectively. (2) The proportion of rural life and livestock to the TN was the highest, accounting for 39.18 % and 21.19 %, respectively. The proportion of livestock to the TP was the highest, accounting for 39.20 %, followed by rural life, accounting for 24.72 %. The results indicated that the pollution fluxes in the BSRB were related to human economic activities and relevant environmental protection measures. (3) The deep learning-based model established to estimate runoff pollution fluxes into the sea had the accuracy of over 90 %. (4) As for contribution rate, in terms of the elevation, the range of 0–100 m had the highest proportion, accounting for 39.65 %. The range of 50–100 km from the coastline had the highest proportion, accounting for 18.11 %. In terms of the district, coastal area has the highest proportion, accounting for 38.00 %. This study revealed the changing trends and driving mechanisms of pollution fluxes into the sea over the past 40 years and established a simplified deep learning-based model for estimating pollution fluxes into the sea. Then, we identified regions with high pollution contribution rate. The results can provide scientific references for the adaptive management of the nearshore areas based on the ecosystem.

Contribution of greenness, air pollution, and residential food environment to excess gestational weight gain: A cross-sectional study in Wuhan, China

ObjectivesOur goal was to explore how greenness, air pollution, and residential food environment were linked to excessive gestational weight gain (EGWG), and to estimate their combined effects on this condition. MethodThis cross-sectional analysis included 51,507 pregnant women from the Wuhan Maternal and Child Health Management Information System between 2016 and 2019. Generalized linear mixed regression models were employed to explore the relationships between greenness, air pollution, residential food environmental exposure, and EGWG; and the combined effects were further estimated by cluster analysis and principal components analysis. ResultWe only found a significant association between convenience store density within the 250 m buffer zone (OR = 1.03 and 95% CI: 1.01,1.05) and EGWG. In terms of air pollution, sulfur dioxide(SO2), particulate matter with a diameter of 10 μm or less(PM10), and particulate matter with a diameter of 2.5 μm or less(PM2.5) were substantially correlated with a higher prevalence of EGWG and higher GWG, with (OR = 1.16 and 95% CI: 1.12,1.21; OR = 1.12 and 95% CI: 1.08,1.16; OR = 1.17 and 95% CI: 1.14,1.21, respectively) per interquartile range(IQR) increase. Cluster analysis revealed the presence of three clusters representing urban exposures. In contrast to urban environment clusters characterized by favourable conditions, those exhibiting elevated air pollution levels, high-density residential food environment and low levels of greenness were found to have increased odds of EGWG (OR = 1.10, 95% CI: 1.03, 1.19). ConclusionThis study emphasizes that exposure to elevated air pollution, high-density residential neighbourhood food environments, and low levels of greenness is a neighbourhood obesogenic environment for pregnant women.

Role of Law in Combatting Air Pollution

Air pollution in urban areas arises from multiple sources, which may vary with location to location and activity to activity of development. Anthropogenic activities such as rampant industrialization, exploitation and over consumption of natural resources, population explosion are major contributors of air pollution. Regulations and legislations adopted are enumerated. The Clean Air Act envisaged guidelines for industries regarding emission. It has been seen and observed that in spite several rules and regulations, not much has been achieved in controlling air pollution particularly in countries like India. India needs to make tough legislations The present paper is an effort to discuss various aspects of air pollution and control legislation in India emphasizing on the history, present scenario, gaps and drawbacks. It also presents legislative controls with judicial response to certain landmark judgments related to air pollution. The down sides related to enforcement mechanism for the effective implementation of environmental laws for air pollution control have been highlighted.