This study aims to investigate the effect of transportation infrastructure on the decrease of NO₂ air pollution during three COVID-19-induced lockdowns in a vast region of France. For this purpose, using Sentinel-5P satellite data, the relative change in tropospheric NO₂ air pollution during the three lockdowns was calculated. The estimation of regional infrastructure intensity was performed using Kernel Density Estimation, being the predictor variable. By performing hotspot–coldspot analysis on the relative change in NO₂ air pollution, significant spatial clusters of decreased air pollution during the three lockdowns were identified. Based on the clusters, a novel spatial index, the Clustering Index (CI) was developed using its Coldspot Clustering Index (CCI) variant as a predicted variable in the regression model between infrastructure intensity and NO₂ air pollution decline. The analysis revealed that during the three lockdowns there was a strong and statistically significant relationship between the transportation infrastructure and the decline index, CCI (r = 0.899, R² = 0.808). The results showed that the largest decrease in NO₂ air pollution was recorded during the first lockdown, and in this case, there was the strongest inverse correlation with transportation infrastructure (r = −0.904, R² = 0.818). Economic and population predictors also explained with good fit the decrease in NO₂ air pollution during the first lockdown: GDP (R² = 0.511), employees (R² = 0.513), population density (R² = 0.837). It is concluded that not only economic-population variables determined the reduction of near-surface air pollution but also the transportation infrastructure. Further studies are recommended to investigate other pollutant gases as predicted variables.

Microplastics are a pollutant of growing concern, capable of harming aquatic organisms and entering the food web. While freshwater microplastic research has expanded in recent years, much remains unknown regarding the sources and delivery pathways of microplastics in these environments. This review aims to address the scientific literature regarding the spatial and temporal factors affecting global freshwater microplastic distributions and abundances. A total of 75 papers, published through June 2021 and containing an earliest publication date of October 2014, was identified by a Web of Science database search. Microplastic spatial distributions are heavily influenced by anthropogenic factors, with higher concentrations reported in regions characterized by urban land cover, high population density, and wastewater treatment plant effluent. Spatial distributions may also be affected by physical watershed characteristics such as slope and elevation (positive and negative correlations with microplastic concentrations, respectively), although few studies address these factors. Temporal variables of influence include precipitation and stormwater runoff (positive correlations) and water flow/discharge (negative correlations). Despite these overarching trends, variations in study results may be due to differing scales or contributing area delineations. Thus, more rigorous and standardized spatial analytical methods are needed. Future research could simultaneously evaluate both spatial and temporal factors and incorporate finer temporal resolutions into sampling campaigns.

Atmospheric nitrogen dioxide (NO₂) is an important reactive gas pollutant harmful to human health. The spatiotemporal coverage provided by traditional NO₂ monitoring methods is insufficient, especially in the suburban and rural areas of north China, which have a high population density and experience severe air pollution. In this study, we implemented a spatiotemporal neural network (STNN) model to estimate surface NO₂ from multiple sources of information, which included satellite and in situ measurements as well as meteorological and geographical data. The STNN predicted NO₂ with high accuracy, with a coefficient of determination (R²) of 0.89 and a root mean squared error of 5.8 μg/m³ for sample-based 10-fold cross-validation. Based on the surface NO₂ concentration determined by the STNN, we analyzed the spatial distribution and temporal trends of NO₂ pollution in north China. We found substantial drops in surface NO₂ concentrations ranging between 9.1% and 33.2% for large cities during the 2020 COVID-19 lockdown when compared to those in 2019. Moreover, we estimated the all-cause deaths attributed to NO₂ exposure at a high spatial resolution of about 1 km, with totals of 6082, 4200, and 18,210 for Beijing, Tianjin, and Hebei Provinces in 2020, respectively. We observed remarkable regional differences in the health impacts due to NO₂ among urban, suburban, and rural areas. Generally, the STNN model could incorporate spatiotemporal neighboring information and infer surface NO₂ concentration with full coverage and high accuracy. Compared with machine learning regression techniques, STNN can effectively avoid model overfitting and simultaneously consider both spatial and temporal correlations of input variables using deep convolutional networks with residual blocks. The use of the proposed STNN model, as well as the surface NO₂ dataset, can benefit air quality monitoring, forecasting, and health burden assessments.

In December 2019, a novel disease, coronavirus disease 19 (COVID-19), emerged in Wuhan, People’s Republic of China. COVID-19 is caused by a novel coronavirus (SARS-CoV-2) presumed to have jumped species from another mammal to humans. This virus has caused a rapidly spreading global pandemic. To date, over 300,000 cases of COVID-19 have been reported in England and over 40,000 patients have died. While progress has been achieved in managing this disease, the factors in addition to age that affect the severity and mortality of COVID-19 have not been clearly identified. Recent studies of COVID-19 in several countries identified links between air pollution and death rates. Here, we explored potential links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. We compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. After controlling for population density, age and median income, we show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. Using detailed UK Biobank data, we further show that PM₂.₅ was a major contributor to COVID-19 cases in England, as an increase of 1 m³ in the long-term average of PM₂.₅ was associated with a 12% increase in COVID-19 cases. The relationship between air pollution and COVID-19 withstands variations in the temporal scale of assessments (single-year vs 5-year average) and remains significant after adjusting for socioeconomic, demographic and health-related variables. We conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study provides a framework to guide both health and emissions policies in countries affected by this pandemic.

Microplastic pollution in the Yangtze River Basin has become a major concern; however, the variations in different environmental compartments are unknown. Here, we compiled published information including detection methods, occurrence, and characterization of microplastics from 624 sampling sites in river water, river sediment, lake and reservoir water, and lake and reservoir sediment in the Yangtze River Basin. The spatial distribution of sampling sites shows fractal pattern and was uniformly concentrated around the main stream of the Yangtze River and the lake geographical zone. Collection, pretreatment, identification, and quantification processes varied among different studies. Non-parametric tests were performed to compare the different microplastic indices. A Pearson correlation analysis was used to study the relationship between microplastic pollution and local socioeconomic conditions. We found that the microplastic size and abundance distribution in river water and lake and reservoir water showed different patterns for different sampling methods, indicating that different methods influenced the results. Population density and urbanization rate are suggested to be important factors influencing the spatial heterogeneity of microplastic abundances in water, rather than in sediment. The microplastic abundances in lake and reservoir water were higher than that in river water in bulk samples. However, microplastic abundances among different sediment environments shows no significant difference. For bulk water samples and sediment samples overall, the proportion of small microplastics (<1 mm, i.e. SMP), fibers, transparent debris, and polypropylene (PP) were 65.1%, 67.8%, 31.8%, and 29.7%, respectively. The microplastic characteristics of lake and reservoir water and sediment were similar, differing from those of river water and sediment. This study provides the first basin scale insight into microplastic occurrence and characteristics in different environments in the Yangtze River Basin.

Aquatic pollution from emerging organic contaminants (EOCs) is of key environmental importance in India and globally, particularly due to concerns of antimicrobial resistance, ecotoxicity and drinking water supply vulnerability. Here, using a broad screening approach, we characterize the composition and distribution of EOCs in groundwater in the Gangetic Plain around Patna (Bihar), as an exemplar of a rapidly developing urban area in northern India. A total of 73 EOCs were detected in 51 samples, typically at ng.L⁻¹ to low μg.L⁻¹ concentrations, relating to medical and veterinary, agrochemical, industrial and lifestyle usage. Concentrations were often dominated by the lifestyle chemical and artificial sweetener sucralose. Seventeen identified EOCs are flagged as priority compounds by the European Commission, World Health Organisation and/or World Organisation for Animal Health: namely, herbicides diuron and atrazine; insecticides imidacloprid, thiamethoxam, clothianidin and acetamiprid; the surfactant perfluorooctane sulfonate (and related perfluorobutane sulfonate, perfluorohexane sulfonate, perfluorooctanoic acid and perfluoropentane sulfonate); and medical/veterinary compounds sulfamethoxazole, sulfanilamide, dapson, sulfathiazole, sulfamethazine and diclofenac. The spatial distribution of EOCs varies widely, with concentrations declining with depth, consistent with a strong dominant vertical flow control. Groundwater EOC concentrations in Patna were found to peak within ∼10 km distance from the River Ganges, indicating mainly urban inputs with some local pollution hotspots. A heterogeneous relationship between EOCs and population density likely reflects confounding factors including varying input types and controls (e.g. spatial, temporal), wastewater treatment infrastructure and groundwater abstraction. Strong seasonal agreement in EOC concentrations was observed. Co-existence of limited transformation products with associated parent compounds indicate active microbial degradation processes. This study characterizes key controls on the distribution of groundwater EOCs across the urban to rural transition near Patna, as a rapidly developing Indian city, and contributes to the wider understanding of the vulnerability of shallow groundwater to surface-derived contamination in similar environments.

Organophosphate esters (OPEs) pose increasing concerns for their widespread distribution in soil environments and potential threat to human health. In this study, we investigated the occurrence and associated risks of seven OPEs in surface soils and the potential influence of human activities on soil OPE contamination in a heavily urbanized region of the Yangtze River Delta in Eastern China. All target OPEs were detected in the soil samples (100% of samples) reflecting their widespread distribution in the study region. The total OPE concentration (the sum of the seven OPEs) ranged from 162.7 to 986.0 ng/g on a dry weight basis, with a mean value of 469.3 ± 178.6 ng/g. Tris (2-butoxyethyl) phosphate was the main compound, accounting for 67–78% of the total OPE concentration. Ecological risk assessment showed that tris(2-chloroisopropyl) phosphate, tris(2,3-dichloropropyl) phosphate, tris(2-butoxyethyl) phosphate, and tris(2-ethylhexyl) phosphate posed a medium potential risk to terrestrial biota (0.1 < risk quotient <1). The human exposure estimation showed insignificant risks to local population. Redundancy analysis revealed that the individual and total OPE contaminations were positively correlated with human activity parameters. The total OPE concentrations were positively correlated to population density (R² = 0.38, P < 0.001), and urban land use percentage (R² = 0.39, P < 0.001), while negatively correlated to forest land use percentage (R² = 0.59, P < 0.001), suggesting a significant contribution of human disturbance to OPE pollution. These results can facilitate OPE contamination control and promote sustainable soil management in urbanized and industrialized regions.

Nanobiomaterials (NBMs) are a special category of nanomaterials used in medicine. As applications of NBMs are very similar to pharmaceuticals, their environmental release patterns are likely similar as well. Different pharmaceuticals were detected in surface waters all over the world. Consequently, there exists a need to identify possible NBM exposure routes into the environment. As the application of many NBMs is only carried out at specific locations (hospitals), average predicted environmental concentrations (PECs) may not accurately represent their release to the environment. We estimated the local release of poly(lactic-co-glycolic acid) (PLGA), which is investigated for their use in drug delivery, to Swiss surface waters by using population data as well as type, size and location of hospitals as proxies. The total mean consumption of PGLA in Switzerland using an explorative full-market penetration scenario was calculated to be 770 kg/year. 105 hospitals were considered, which were connected to wastewater treatment plants and the receiving water body using graphic information system (GIS) modelling. The water body dataset contained 20,167 river segments and 210 lake polygons. Using the discharge of the river, we were able to calculate the PECs in different river segments. While we calculated high PLGA releases of 2.24 and 2.03 kg/year in large cities such as Geneva or Zurich, the resulting local PECs of 220 and 660 pg/l, respectively, were low due to the high river discharge (330 and 97 m³/s). High PLGA concentrations (up to 7,900 pg/l) on the other hand were calculated around smaller cities with local hospitals but also smaller receiving rivers (between 0.7 and 1.9 m³/s). Therefore, we conclude that population density does not accurately predict local concentration hotspots of NBMs, such as PLGA, that are administered in a hospital context. In addition, even at the locations with the highest predicted PLGA concentrations, the expected risk is low.

Five endocrine-disrupting compounds (EDCs) were determined in four urban wastewater treatment plants (WWTPs) of the Metropolitan Area of Monterrey (MAM) in two seasonal periods (winter and summer). The MAM, one of the most urbanized areas in Mexico, is characterized by high industrial activity and population density, leading to extensive use of several EDCs. In the MAM, ∼90% of urban and industrial wastewater is treated in WWTPs, where EDCs can be partially eliminated. In this work, dissolved levels of 17β-estradiol (E2), 17α-ethinyl estradiol (EE2), bisphenol A (BPA), 4-nonylphenol (4NP), and 4-tert-octylphenol (4TOP) in wastewater were determined. The EDCs’ determination was carried out through solid-phase extraction (SPE) and gas chromatography coupled to mass spectrometry (GC-MS). High EDCs levels (0.4–450 ng/L) were found in the influents of WWTPs, while concentrations in the effluents ranged from 0.2 to 26.8 ng/L, with E2, EE2, and 4TOP being the most persistent. The Spearman correlation analysis revealed the association between E2 and EE2 (r = 0.4835, p < 0.05), and between BPA and 4NP (r = 0.5180, p < 0.05), suggesting that these EDCs have similar sources. Also, E2, BPA, and 4TOP were positively correlated with the chemical oxygen demand (COD), biochemical oxygen demand (BOD), and total suspended solids (TSS) (r = 0.4080–0.5694, p < 0.05), indicating the association of the EDCs with the organic matter in the wastewater. The factor analysis confirmed the significant correlation of COD, BOD, TSS, temperature, and pH with the high occurrence of 4TOP during the summer. It was also confirmed that summer warmer temperatures favored the removal of BPA and 4NP in the studied WWTPs. Finally, the studied sites were classified by cluster analysis in three groups, revealing the impact that seasonality has on the behavior of the selected EDCs.

Particulate matter with an aerodynamic equivalent dimeter less than 2.5 μm (PM₂.₅) and ozone (O₃) are major air pollutants, with coupled and complex relationships. The control of both PM₂.₅ and O₃ pollution requires the identification of their common influencing factors, which has rarely been attempted. In this study, land use regression (LUR) models based on the least absolute shrinkage and selection operator were developed to estimate PM₂.₅ and O₃ concentrations in China's Pearl River Delta region during 2019. The common factors in the tradeoffs between the two air pollutants and their synergistic effects were analyzed. The model inputs included spatial coordinates, remote sensing observations, meteorological conditions, population density, road density, land cover, and landscape metrics. The LUR models performed well, capturing 54–89% and 42–83% of the variations in annual and seasonal PM₂.₅ and O₃ concentrations, respectively, as shown by the 10-fold cross validation. The overlap of variables between the PM₂.₅ and O₃ models indicated that longitude, aerosol optical depth, O₃ column number density, tropospheric NO₂ column number density, relative humidity, sunshine duration, population density, the percentage cover of forest, grass, impervious surfaces, and bare land, and perimeter-area fractal dimension had opposing effects on PM₂.₅ and O₃. The tropospheric formaldehyde column number density, wind speed, road density, and area-weighted mean fractal dimension index had complementary effects on PM₂.₅ and O₃ concentrations. This study has improved our understanding of the tradeoff and synergistic factors involved in PM₂.₅ and O₃ pollution, and the results can be used to develop joint control policies for both pollutants.