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.

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.

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.

Due to the complex sources and fate of perfluoroalkyl substance (PFAS), their source apportionment in the environment remains a challenge. A data set of 11 straight-chain PFAS in 139 samples of fish in the Great Lakes was analyzed using positive matrix factorization (PMF) to investigate their primary sources, whose spatial variations were examined against the surrounding environmental factors. PMF analysis produced five fingerprints. Factor 1 (72% of Σ₁₁PFAS, dominated by PFOS) probably represented emissions from primary sources (such as consumer products) and secondary sources (precursors), and increased in average abundance from west to east across the Great Lakes. Factor 2 (13% of Σ₁₁PFAS) and factor 3 (7% of Σ₁₁PFAS), highly loaded with long-chain PFAS and PFNA, respectively, were thought to represent PVDF manufacture or processing in metal plating. They showed higher contributions in sparsely populated Lakes Superior and Huron. Factor 4 (5% of Σ₁₁PFAS, highly loaded with PFOS and PFHxS) presented hot spots near current and former air force bases, suggesting it was related to aqueous film-forming foams (AFFFs). Factor 5 (4% of Σ₁₁PFAS) contained primarily PFOS and PFOSA, which may imply metabolism of precursors (PFOSA) to PFOS in vivo. Unexpectedly, the spatial trends of the five sources all showed abnormally low values near the more urbanized Chicago and Milwaukee in Lake Michigan, which may be due to their unique wastewater and stormwater infrastructure or may arise from atmospheric transport of precursors. Our study indicated that PMF was an effective tool to identify sources of PFAS in fish despite absorption, distribution, metabolism, and excretion (ADME) processes which might alter fingerprints in fish relative to their surrounding environment.

The increasing adoption of intelligent transportation system (ITS) data in smart-city initiatives worldwide has offered unprecedented opportunities for improving transportation air quality management. In this paper, we demonstrate the effective use of ITS and other traffic data to develop a link-level and hourly-based dynamic vehicle emission inventory. Our work takes advantage of the extensive ITS infrastructure deployed in Nanjing, China (6600 km2) that offers high-resolution, multi-source traffic data of the road network. Improved than conventional emission inventories, the ITS data empower the strength of revealing significantly temporal and spatial heterogeneity of traffic dynamics that pronouncedly impacts traffic emission patterns. Four urban districts account for only 4% of the area but approximately 30%–40% of vehicular emissions (e.g., CO2 and air pollutants). Owing to the detailed resolution of road network traffic, two types of emission hotspots are captured by the dynamic emission inventory: those in the urban area dominated by urban passenger traffic, and those along outlying highway corridors reflecting inter-city freight transportation (especially in terms of NOX). Fine-grained quantification of emissions reductions from traffic restriction scenarios is explored. ITS data-driven emission management systems coupled with atmospheric models offer the potential for dynamic air quality management in the future.

Africa's economy is growing faster than any other continent and it has been estimated that the middle class in Africa now exceeds 350 million people. This has meant a parallel increase in the importation of consumer goods and in the implementation of communication and information technologies (ICT), but also in the generation of large quantities of e-waste. However, inadequate infrastructure development remains a major constraint to the continent's economic growth and these highly toxic residues are not always adequately managed. Few studies have been conducted to date assessing the possible association between socioeconomic development factors, including e-waste generation, and blood levels of inorganic elements in African population. To disclose the role of geographical, anthropogenic, and socioeconomic development determinants on the blood levels of Ag, Al, As, Be, Cd, Co, Cr, Hg, Ni, Pb, Sb, and V —all of them frequently found in e-waste—, an immigrant population-based study was made including a total of 245 subjects from 16 countries recently arrived to the Canary Islands (Spain). Women presented higher levels of blood elements than men, and Northern Africans (Moroccans) were the most contaminated. People from low-income countries exhibited significantly lower blood levels of inorganic elements than those from middle-income countries. We found a significant association between the use of motor vehicles and the implementation of information and communication technologies (ICT) and the level of contamination. Immigrants from the countries with a high volume of imports of second-hand electronic equipment, telephone and internet use had higher levels of inorganic elements. In general terms, the higher level of economic development the higher the blood levels of inorganic pollutants, suggesting that the economic development of Africa, in parallel to e-waste generation and the existence of informal recycling sites, have directly affected the level of contamination of the population of the continent.

The continued growth of human activity and infrastructure has translated into a widespread increase in light pollution. Natural daylight and moonlight cycles play a fundamental role for many organisms and ecological processes, so an increase in light pollution may have profound effects on communities and ecosystem services. Studies assessing ecological light pollution (ELP) effects on sandy beach organisms have lagged behind the study of other sources of disturbance. Hence, we assessed the influence of this stressor on locomotor activity, foraging behavior, absorption efficiency and growth rate of adults of the talitrid amphipod Orchestoidea tuberculata. In the field, an artificial light system was assembled to assess the local influence of artificial light conditions on the amphipod's locomotor activity and use of food patches in comparison to natural (ambient) conditions. Meanwhile in the laboratory, two experimental chambers were set to assess amphipod locomotor activity, consumption rates, absorption efficiency and growth under artificial light in comparison to natural light-dark cycles. Our results indicate that artificial light have significantly adverse effects on the activity patterns and foraging behavior of the amphipods, resulting on reduced consumption and growth rates. Given the steady increase in artificial light pollution here and elsewhere, sandy beach communities could be negatively affected, with unexpected consequences for the whole ecosystem.

Living in an urban environment is associated with an increased prevalence of specific mental health disorders, particularly schizophrenia. While many factors have been discussed as possible mediators of this association, most researchers favour the hypothesis that urban living stands as a proxy for an increased exposure to social stress. This factor has been recognized as one of the most powerful causes for the development of mental disorders, and appears to correlate with the markedly increased incidence of schizophrenia in urban minority groups. However, the hypothesis that the general urban population is exposed to increased levels of social stress has to be validated. Pursuing the goal of understanding how social stress acts as a risk factor for mental disorder in urban populations must include factors like social conditions, environmental pollutants, infrastructure and economic issues.

Wastewater-based epidemiology (WBE) has emerged as a valuable approach for forecasting disease outbreaks in developed countries with a centralized sewage infrastructure. On the other hand, due to the absence of well-defined and systematic sewage networks, WBE is challenging to implement in developing countries like Bangladesh where most people live in rural areas. Identification of appropriate locations for rural Hotspot Based Sampling (HBS) and urban Drain Based Sampling (DBS) are critical to enable WBE based monitoring system. We investigated the best sampling locations from both urban and rural areas in Bangladesh after evaluating the sanitation infrastructure for forecasting COVID-19 prevalence. A total of 168 wastewater samples were collected from 14 districts of Bangladesh during each of the two peak pandemic seasons. RT-qPCR commercial kits were used to target ORF1ab and N genes. The presence of SARS-CoV-2 genetic materials was found in 98% (165/168) and 95% (160/168) wastewater samples in the first and second round sampling, respectively. Although wastewater effluents from both the marketplace and isolation center drains were found with the highest amount of genetic materials according to the mixed model, quantifiable SARS-CoV-2 RNAs were also identified in the other four sampling sites. Hence, wastewater samples of the marketplace in rural areas and isolation centers in urban areas can be considered the appropriate sampling sites to detect contagion hotspots. This is the first complete study to detect SARS-CoV-2 genetic components in wastewater samples collected from rural and urban areas for monitoring the COVID-19 pandemic. The results based on the study revealed a correlation between viral copy numbers in wastewater samples and SARS-CoV-2 positive cases reported by the Directorate General of Health Services (DGHS) as part of the national surveillance program for COVID-19 prevention. The findings of this study will help in setting strategies and guidelines for the selection of appropriate sampling sites, which will facilitate in development of comprehensive wastewater-based epidemiological systems for surveillance of rural and urban areas of low-income countries with inadequate sewage infrastructure.

Nitrate pollution in oxygenated karst aquifers is common due to nitrification and anthropogenic inputs. However, the shift of nitrogen sources influenced by enhanced rural tourism activities and land use changes are not well understood. In this study, hydrochemistry and dual nitrate isotopes of water samples from a rural karst basin in Chongqing, southwestern China were employed to investigate the nitrate fate and its decadal change during the periods from 2007–2008 and 2017–2019. The results showed that δ¹⁵N–NO₃ and δ¹⁸O–NO₃ values at the groundwater basin resurgence averaged 9 ± 3.4‰ and 2.5 ± 3.4‰, respectively, with a mean NO₃⁻ concentration of 19.7 ± 5.4 mg/L in 2017–2019, clearly exceeding natural background levels. The dual isotope results suggested that nitrification occurred at the sampled sites. From 2007–2008 to 2017–2019, the mean δ¹⁵N–NO₃ values from the primary sink point and the resurgence of the underground river water samples increased from −0.2 ± 2.1 to 11.2 ± 4.8‰, 4.2 ± 0.9 to 9.0 ± 3.4‰, respectively. A Bayesian mixing model in R (MixSIAR) based on the isotopes revealed that soil organic nitrogen, and manure and sewage proportions for the groundwater increased by 34% and 23%, respectively, while chemical fertilizer and atmospheric precipitation proportions decreased by 32% and 25%, respectively. These decadal changes resulted from reforestation practices and enhanced rural tourism activities in the basin, which were evidenced by the change of land use patterns. The elevated nitrogen load from the rapid development of rural tourism is likely to increase this contamination in the near future if the infrastructure cannot meet the demands. The results from this study could contribute to minimizing environmental health risks in drinking water when rural tourism activities are increasing.