Many cities across the world face the challenge of severe fine particulate matter (PM₂.₅) pollution. Among the many factors that affect PM₂.₅ pollution, there is an increasing interest in the impacts of urban structure. However, quantifying these impacts in China has been difficult due to differences of study area and scale in existing research, as well as limited sample sizes. Here, we conducted a continental study focusing on 301 prefectural cities in mainland China to investigate the effects of urban structure, including urban size and urban compactness, on PM₂.₅ concentrations. Based on PM₂.₅ raster and land cover data, we used quantile regression and a general multilinear model to estimate the effects and relative contributions of urban size and urban compactness on urban PM₂.₅ pollution, with explicit consideration for pollution level, urban size and geographical location. We found: (1) nationwide, the larger and more compact that cities were, the heavier the PM₂.₅ pollution tended to be. Additionally, this relationship became stronger with increasing levels of pollution. (2) In general, urban size played a more important role than urban form, and there were no significant interactive effects between the two metrics on urban PM₂.₅ concentrations at the national scale. (3) The impacts of urban size and form varied by city size and geographical location. The impacts of urban size were only significant for small or medium-large cities but not for large cities. Among large cities, only urban form had a significantly positive effect on urban PM₂.₅ concentrations. The further north and west that cities were, the more dependent PM₂.₅ pollution was on urban form, whereas the further south and east that cities were, the greater the impact of urban size. These results provide insights into how urban design and planning can be used to alleviate air pollution.

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.

After decades of imposed regulations about reducing the primary emissions of persistent organic pollutants (POPs), these pollutants are still present in the environment. Soils are important repositories of such persistent semivolatile organic contaminants (SVOCs), and it is assumed that SVOCs sequestered in these reservoirs are being re-mobilized due to anthropogenic influence. In this study, concentrations of organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs) in soil and air, their fugacities, fluxes and the soil-air partition coefficient (KSA) were determined for three different land cover types (glacial, remote/mountainous and urban) of the Lesser Himalayan Region (LHR). The concentrations of OCPs, PCBs and PBDEs in soils and air ranged between 0.01 and 2.8, 0.81–4.8, 0.089–0.75 ng g⁻¹; 0.2–106, 0.027–182, and 0.011–7.26 pg m⁻³, respectively. The levels of SVOCs in the soil were correlated with soil organic matter (SOM) indicating that SOM is a substrate for the organic pollutants in soils. The Clausius-Clapeyron plots between ln P and inverse of temperature (1000/T) suggested that long range atmospheric transport was the major input source of PBDEs and higher chlorinated PCBs over the LHR. The uneven and wide distribution of local sources in LHR and up-slope enrichment of SVOCs explained the spatial variability and altitudinal patterns. The soils near mountain and urban lakes act as local sinks of SVOCs such as β-HCH, pp΄-DDT, CB-28, -118, −153, BDE-47, -99, and −154, with soil-air exchange fluxes tending more toward deposition. However, the soils near glacial lakes acted as local sources of more volatile congeners of α-HCH, γ-HCH, op′-DDT, pp′-DDE and lower to medium chlorinated PCBs such as CB-18, -28, −53, −42 and BDE-47, -99, with soil-air exchange tending more toward volatilization flux.

Land use/Land cover (LULC) associated with Cryptosporidium sp. and Giardia sp. quantification and distribution can provide identification of the environmental circulation patterns of these parasites. The aim of this research was to relate the occurrence and circulation of these parasites to the LULC watershed with poor sanitation infrastructure and livestock as important economic activity. The study involved 11 municipalities in the state of São Paulo, located in southeastern Brazil. Sampling was carried out at the catchment sites of each water supply on a monthly basis, starting in December 2014 and lasting until November 2015, totalizing 128 samples. Protozoans were quantified according to the 1623.1 US. EPA Method. For watershed delimitation, the hydrographic network was extracted from the hydrology tool of ArcGIS 10.1. The frequency of occurrence of these pathogens and the high concentrations were evidenced in the municipality with the largest urban area (16.2%) and intense livestock activity (39%) near the catchment site. The municipality that showed the lowest frequency of occurrence presented the smallest urban area (0.87%) and absence of livestock activity near the catchment site. The high concentration of pathogens suggests a correlation between the impact on water supply networks and river basin degradation caused by urban activity and livestock.

Microplastic pollution has received considerable attention in marine systems, but recent work shows substantial plastic pollution also occurs in freshwater ecosystems. Most freshwater research has focused on large rivers and lakes, but small streams are the primary interface between land, where plastic is used, and drainage networks. We examined variation in the amount and form of plastic occurring in small streams spanning an urbanisation gradient. All streams contained microplastics with concentrations similar to that found in larger systems (up to 303 particles m−3 in water and 80 particles kg−1 in sediment). The most abundant types were fragments and small particles (63–500 μm). Chemical types of plastic were quite variable and often not predictable based on size, form and colour. Variation in microplastic abundance across streams was high, but only partially explained by catchment scale parameters. There was no relationship between human population density or combined stormwater overflows and microplastic abundance. Residential land cover was related to microplastic abundance, but explanatory power was low. Our results suggest local-scale factors may be more important than catchment-scale processes in determining microplastic pollution in small streams.

Exposure to fine particulate matter (PM₂.₅) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM₂.₅ in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM₂.₅ concentrations in China, methods for generating reliable historical PM₂.₅ exposure, especially prior to the construction of PM₂.₅ monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM₂.₅ levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM₂.₅ measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM₂.₅ concentrations. The overall model CV R² and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013–2015) shown that this model can accurately predict historical PM₂.₅ concentrations at the monthly (R² = 0.74, RPE = 27.6%), seasonal (R² = 0.78, RPE = 21.2%) and annual (R² = 0.76, RPE = 16.9%) level. The annual mean predicted PM₂.₅ concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM₂.₅ concentrations for epidemiological studies on PM₂.₅ health effects in China.

Critical levels (CLEs) of atmospheric ammonia based on biodiversity changes have been mostly calculated using small-scale single-source approaches, to avoid interference by other factors, which also influence biodiversity. Thus, it is questionable whether these CLEs are valid at larger spatial scales, in a multi- disturbances context. To test so, we sampled lichen diversity and ammonia at 80 sites across a region with a complex land-cover including industrial and urban areas. At a regional scale, confounding factors such as industrial pollutants prevailed, masking the CLEs. We propose and use a new tool to calculate CLEs by stratifying ammonia concentrations into classes, and focusing on the highest diversity values. Based on the significant correlations between ammonia and biodiversity, we found the CLE of ammonia for Mediterranean evergreen woodlands to be 0.69 μg m−3, below the previously accepted value of 1.9 μg m−3, and below the currently accepted pan-European CLE of 1.0 μg m−3.

Groundwater nitrate (NO₃⁻) pollution is a worldwide environmental problem. Therefore, identification and partitioning of its potential sources are of great importance for effective control of groundwater quality. The current study was carried out to identify the potential sources of groundwater NO₃⁻ pollution and determine their apportionment in different land use/land cover (LULC) types in a traditional agricultural area, Weining Plain, in Northwest China. Multiple hydrochemical indices, as well as dual NO₃⁻ isotopes (δ¹⁵N–NO₃ and δ¹⁸O–NO₃), were used to investigate the groundwater quality and its influencing factors. LULC patterns of the study area were first determined by interpreting remote sensing image data collected from the Sentinel-2 satellite, then the Bayesian stable isotope mixing model (MixSIAR) was used to estimate proportional contributions of the potential sources to groundwater NO₃⁻ concentrations. Groundwater quality in the study area was influenced by both natural and anthropogenic factors, with anthropological impact being more important. The results of LULC revealed that the irrigated land is the dominant LULC type in the plain, covering an area of 576.6 km² (57.18% of the total surface study area of the plain). On the other hand, the results of the NO₃⁻ isotopes suggested that manure and sewage (M&S), as well as soil nitrogen (SN), were the major contributors to groundwater NO₃⁻. Moreover, the results obtained from the MixSIAR model showed that the mean proportional contributions of M&S to groundwater NO₃⁻ were 55.5, 43.4, 21.4, and 78.7% in the forest, irrigated, paddy, and urban lands, respectively. While SN showed mean proportional contributions of 29.9, 43.4, 61.5, and 12.7% in the forest, irrigated, paddy, and urban lands, respectively. The current study provides valuable information for local authorities to support sustainable groundwater management in the study region.

Microplastics are emerging contaminants ubiquitously distributed in the environment, with rivers acting as their main mode of transport in surface freshwater systems. However, the relative importance of hydrologic processes and source-related variables for benthic microplastic distribution in river sediments is not well understood. We therefore sampled and characterized microplastics in river sediments across the Meramec River watershed (eastern Missouri, United States) and applied a hydrologic modeling approach to estimate the relative importance of river discharge, river sediment load, land cover, and point source pollution sites to understand how these environmental factors affect microplastic distribution in benthic sediments. We found that the best model for the Meramec River watershed includes both source-related variables (land cover and point sources) but excludes both hydrologic transport-related variables (discharge and sediment load). Prior work has drawn similar and dissimilar conclusions regarding the importance of anthropogenic versus hydrologic variables in microplastic distribution, though we acknowledge that comparisons are limited by methodological differences. Nevertheless, our findings highlight the complexity of microplastic pollution in freshwater systems. While generating a universal predictive model might be challenging to achieve, our study demonstrates the potential of using a modeling approach to determine the controlling factors for benthic microplastic distribution in fluvial systems.

The identification of nitrate (NO₃⁻) sources and biogeochemical transformations is critical for understanding the different nitrogen (N) pathways, and thus, for controlling diffuse pollution in groundwater affected by livestock and agricultural activities. This study combines chemical data, including environmental isotopes (δ²HH₂O, δ¹⁸OH₂O, δ¹⁵NNO₃, and δ¹⁸ONO₃), with land use/land cover data and a Bayesian isotope mixing model, with the aim of reducing the uncertainty when estimating the contributions of different pollution sources. Sampling was taken from 53 groundwater sites in Comarca Lagunera, northern Mexico, during 2018. The results revealed that the NO₃⁻ (as N) concentration ranged from 0.01 to 109 mg/L, with more than 32% of the sites exceeding the safe limit for drinking water quality established by the World Health Organization (10 mg/L). Moreover, according to the groundwater flow path, different biogeochemical transformations were observed throughout the study area: microbial nitrification was dominant in the groundwater recharge areas with elevated NO₃⁻ concentrations; in the transition zones a mixing of different transformations, such as nitrification, denitrification, and/or volatilization, were identified, associated to moderate NO₃⁻ concentrations; whereas in the discharge area the main process affecting NO₃⁻ concentrations was denitrification, resulting in low NO₃⁻ concentrations. The results of the MixSIAR isotope mixing model revealed that the application of manure from concentrated animal-feeding operations (∼48%) and urban sewage (∼43%) were the primary contributors of NO₃⁻ pollution, whereas synthetic fertilizers (∼5%), soil organic nitrogen (∼4%), and atmospheric deposition played a less important role. Finally, an estimation of an uncertainty index (UI90) of the isotope mixing results indicated that the uncertainties associated with atmospheric deposition and NO₃⁻−fertilizers were the lowest (0.05 and 0.07, respectively), while those associated with manure and sewage were the highest (0.24 and 0.20, respectively).