Increased interest in backyard food production has drawn attention to the risks associated with urban trace element contamination, in particular lead (Pb) that was used in abundance in Pb-based paints and gasoline. Here we examine the sources, pathways and risks associated with environmental Pb in urban gardens, domestic chickens and their eggs. A suite of other trace element concentrations (including As, Cd, Cr, Cu, Hg, Mn, Ni, Pb, Zn) are reported from the sampled matrices. Sixty-nine domestic chickens from 55 Sydney urban gardens were sampled along with potential sources (feed, soil, water), blood Pb concentrations and corresponding concentrations in eggs. Age of the sampled chickens and house age was also collected. Commercial eggs (n = 9) from free range farms were analysed for comparative purposes. Study outcomes were modelled using the large Australian VegeSafe garden soil database (>20,000 samples) to predict which areas of inner-city Sydney, Melbourne and Brisbane are likely to have soil Pb concentrations unsuitable for keeping backyard chickens. Soil Pb concentrations was a strong predictor of chicken blood and egg Pb (p=<0.00001). Almost 1 in 2 (n = 31/69) chickens had blood Pb levels >20 μg/dL, the level at which adverse effects may be observed. Older homes were correlated with higher chicken blood Pb (p = 0.00002) and egg Pb (p = 0.005), and younger chickens (<12 months old) had greater Pb concentrations, likely due to increased Pb uptake during early life development. Two key findings arose from the study data: (i) in order to retain chicken blood Pb below 20 μg/dL, soil Pb needs to be < 166 mg/kg; (ii) to retain egg Pb < 100 μg/kg (i.e. a food safety benchmark value), soil Pb needs to be < 117 mg/kg. These concentrations are significantly lower than the soil Pb guideline of 300 mg/kg for residential gardens. This research supports the conclusion that a large number of inner-city homes may not be suitable for keeping chickens and that further work regarding production and consumption of domestic food is warranted.

Most amyotrophic lateral sclerosis (ALS) cases are sporadic (∼90%) and environmental exposures are implicated in their etiology. Large industrial facilities are permitted the airborne release of certain chemicals with hazardous properties and report the amounts to the US Environmental Protection Agency (EPA) as part of its Toxics Release Inventory (TRI) monitoring program. The objective of this project was to identify industrial chemicals released into the air that may be associated with ALS etiology. We geospatially estimated residential exposure to contaminants using a de-identified medical claims database, the SYMPHONY Integrated Dataverse®, with ∼26,000 nationally distributed ALS patients, and non-ALS controls matched for age and gender. We mapped TRI data on industrial releases of 523 airborne contaminants to estimate local residential exposure and used a dynamic categorization algorithm to solve the problem of zero-inflation in the dataset. In an independent validation study, we used residential histories to estimate exposure in each year prior to diagnosis. Air releases with positive associations in both the SYMPHONY analysis and the spatio-temporal validation study included styrene (false discovery rate (FDR) 5.4e-5), chromium (FDR 2.4e-4), nickel (FDR 1.6e-3), and dichloromethane (FDR 4.8e-4). Using a large de-identified healthcare claims dataset, we identified geospatial environmental contaminants associated with ALS. The analytic pipeline used may be applied to other diseases and identify novel targets for exposure mitigation. Our results support the future evaluation of these environmental chemicals as potential etiologic contributors to sporadic ALS risk.

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.

Airborne bacteria may absorb the substance from the atmospheric particles and play a role in biogeochemical cycling. However, these studies focused on a few culturable bacteria and the samples were usually collected from one site. The metabolic potential of a majority of airborne bacteria on a regional scale and their driving factors remain unknown. In this study, we collected particulates with aerodynamic diameter ≤2.5 μm (PM₂.₅) from 8 cities that represent different regions across China and analyzed the samples via high-throughput sequencing of 16S rRNA genes, quantitative polymerase chain reaction (qPCR) analysis, and functional database prediction. Based on the FAPROTAX database, 326 (80.69%), 191 (47.28%) and 45 (11.14%) bacterial genera are possible to conduct the pathways of carbon, nitrogen, and sulfur cycles, respectively. The pathway analysis indicated that airborne bacteria may lead to the decrease in organic carbon while the increase in ammonium and sulfate in PM₂.₅ samples, all of which are the important components of PM₂.₅. Among the 19 environmental factors studied including air pollutants, meteorological factors, and geographical conditions, PM₂.₅ concentration manifested the strongest correlations with the functional genes for the transformation of ammonium and sulfate. Moreover, the PM₂.₅ concentration rather than the sampling site will drive the distribution of functional genera. Thus, a bi-directional relationship between PM₂.₅ and bacterial metabolism is suggested. Our findings shed light on the potential bacterial pathway for the biogeochemical cycling in the atmosphere and the important role of PM₂.₅, offering a new perspective for atmospheric ecology and pollution control.

Nitrogen (N) is an essential nutrient but may become a pollution source in the environment when the N concentration exceeds a certain threshold for humans and nature. Nitrate is a major N species in river water with notable spatial and temporal variations under the influences of natural factors and anthropogenic N inputs. We analyzed the relationship between riverine N (focusing on nitrate) concentration and various factors (land use, climate, basin topography, atmospheric N deposition, agricultural N sources and human-derived N) in 104 rivers located throughout the Japanese Archipelago except small remote islands. We aimed to better understand processes and mechanisms to explain the spatial and temporal changes in riverine nitrate concentration. A publicly available river water quality database observed in the 1980s (1980–1989) and 2000s (2000–2009) was used. This study is the first to evaluate the long-term scale of 20 years in the latter half of Japan's economic growth period at the national level. A geographic information system (GIS) was employed to determine average values of each variable collected from multiple sources of statistical data. We then performed regression analysis and structural equation modeling (SEM) for each period. The forestland area influenced by the basin topography, climate (i.e., air temperature) and other land uses (i.e., farmland and urban area) played a major role in decreasing nitrate concentrations in both the 1980s and 2000s. Atmospheric N deposition (especially N oxides) and agricultural N sources (fertilizer and manure) were also significant variables regarding the spatial variations in riverine nitrate concentrations. The SEM results suggested that human-derived N (via food consumption) intensified by demographic shifts during the 2000s increased riverine nitrate concentrations over other variables within the context of spatial variation. These findings facilitate better decision making regarding land use, agricultural practices, pollution control and individual behaviors toward a sustainable society.

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

Strobilurins are popular fungicides used in agriculture on a global scale. Due to their widespread use as agrochemicals, they can enter aquatic environments at concentrations that can elicit adverse effects in organisms. This review synthesizes the current state of knowledge regarding the toxic effects of strobilurin fungicides on aquatic species, including algal species, Daphnia magna, and fish species, to determine risk to aquatic organisms and ecosystems. Data show that the toxicities of strobilurins vary widely across aquatic species. Strobilurins bind cytochrome bc1 in mitochondrial complex III in fungi, and as such, research in aquatic species has focused on mitochondria-related endpoints following exposures to strobilurins. In fish, studies into the activities of mitochondrial complexes and the expression of genes involved in the electron transfer chain have been conducted, converging on the theme that mitochondrial complexes and their enzymes are impaired by strobilurins. In general, the order of toxicity of strobilurins for fish species are pyraoxystrobin > pyraclostrobin ≈ trifloxystrobin > picoxystrobin > kresoxim-methyl > fluoxastrobin > azoxystrobin. In addition to mitochondrial toxicity, studies also report genotoxicity, immunotoxicity, cardiotoxicity, neurotoxicity, and endocrine disruption, and each of these events can potentially impact whole organism-level processes such as development, reproduction, and behavior. Screening data from the US Environmental Protection Agency ToxCast database supports the hypothesis that these fungicides may act as endocrine disruptors, and high throughput data suggest estrogen receptor alpha and thyroid hormone receptor beta can be activated by some strobilurins. It is recommended that studies investigate the potential for endocrine disruption by strobilurins more thoroughly in aquatic species. Based on molecular, physiological, and developmental outcomes, a proposed adverse outcome pathway is presented with complex III inhibition in the electron transfer chain as a molecular initiating event. This review comprehensively addresses sub-lethal toxicity mechanisms of strobilurin fungicides, important as the detection of strobilurins in aquatic environments suggests exposure risks in wildlife.

PM₂.₅ and PM₁₀ fugitive dust samples from multiple sources (construction, demolition, industrial, agricultural fields, and bare ground) were collected in triplicate for each size bin, from 18 distinct locations in and around Bhopal, central India. The dust samples were dried, sieved, and re-suspended in a chamber fitted with a suitable sampling system, to collect PM₂.₅ and PM₁₀ samples onto Teflon and Quartz filters. The filters were subjected to gravimetric and chemical analyses. Trace elements, water-soluble ions, and thermal-optical carbon fractions were quantified using a variety of analyses. These species were then used to develop PM₁₀ and PM₂.₅ chemical source profiles of the fugitive dust sources. As expected, crustal species were abundant in all source categories. For industrial dust, Fe contribution to mass in both size fractions was about 11.4% and above the upper continental crustal abundance. Further, the source profiles generated for each source were different from their counterparts in the US EPA SPECIATE database and profiles reported in literature. Thus, it will be useful to utilize profiles generated in this study to enhance receptor model performance for the study region. However, collinearity analysis of the profiles revealed that PM₁₀ agricultural and bare ground dust; and PM₂.₅ construction and demolition dust profile pairs may not be separated by receptor models. Finally, a human health risk assessment revealed that construction and industrial dust may pose significant risk to the population. The Incremental Lifetime Cancer Risk (ILCR) metric revealed that adults (2 × 10⁻⁵) and children (1 × 10⁻⁵) were susceptible to cancer risk from exposure to metals in PM₂.₅ fugitive dust. Further, children were more vulnerable than adults. This finding merits further investigation of oxidation state and solubility/bioavailability of Cr and Ni in fugitive dusts.

Water pollution is an urgent problem that needs to be controlled via green transformation and the development of the Yangtze River Economic Belt (YREB). Based on the water pollutant discharge and socio-economic database of prefecture-level cities in the YREB from 2011 to 2015, this study explores the spatiotemporal variations in water pollutant discharge in the YREB via two main indicators: chemical oxygen demand (COD) and ammonia nitrogen (NH₃–N). Further, the spatial effects and determinants of water pollutant discharge are quantitatively estimated. The results show that (1) the water pollutant discharge in the YREB has decreased significantly, with the COD and NH₃–N discharge reduced by 10.46% and 10.79%, respectively, and the discharge reduction in the lower reaches was the most prominent; (2) the spatial pattern of water pollutant discharge in the YREB was generally stable and partially improved, and cities with a high rate of water pollutant reduction in the YREB were distributed in the main stream region of the Yangtze River and the intersection of the main stream and tributaries; (3) spatial effects had a significant impact on water pollutant discharge in the YREB, with regional cooperation and economic radiation through environmental management and control initially showing a combined reduction trend in regional water pollutants; and (4) determinants of population size and agricultural economic share declined to varying degrees at the end of the study period, although the urbanization level continued to increase, indicating that urbanization in the YREB occurred too quickly and that water pollutant discharge reduction was limited. However, economic development leading to the deterioration of the water environment was alleviated. In addition, foreign direct investment (FDI) inflows and rapid industrialization processes must be monitored to increase the reduction in characteristic water pollutants.

Urban environments are characterized by multiple exposures that may influence body mass index (BMI) growth in early life. Previous studies are few, with inconsistent results and no evaluation of simultaneous exposures. Thus, this study aimed to assess the associations between exposure to air pollution, green spaces and built environment characteristics, and BMI growth trajectories from 0 to 5 years. This longitudinal study used data from an electronic primary care health record database in Catalonia (Spain), including 79,992 children born between January 01, 2011 and December 31, 2012 in urban areas and followed until 5 years of age. Height and weight were measured frequently during childhood and BMI (kg/m²) was calculated. Urban exposures were estimated at census tract level and included: air pollution (nitrogen dioxide (NO₂), particulate matter <10 μm (PM₁₀) and <2.5 μm (PM₂.₅₎), green spaces (Normalized Difference Vegetation Index (NDVI) and % green space) and built environment (population density, street connectivity, land use mix, walkability index). Individual BMI trajectories were estimated using linear spline multilevel models with several knot points. In single exposure models, NO₂, PM₁₀, PM₂.₅, and population density were associated with small increases in BMI growth (e.g. β per IQR PM₁₀ increase = 0.023 kg/m², 95%CI: 0.013, 0.033), and NDVI, % of green spaces and land use mix with small reductions in BMI growth (e.g. β per IQR % green spaces increase = −0.015 kg/m², 95%CI: −0.026, −0.005). These associations were strongest during the first two months of life. In multiple exposure models, most associations were attenuated, with only those for PM₁₀ and land use mix remaining statistically significant. This large longitudinal study suggests that early life exposure to air pollution, green space and built environment characteristics may be associated with small changes in BMI growth trajectories during the first years of life, and that it is important to account for multiple exposures in urban settings.