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.

Marine Protected Areas (MPAs) are designated to enable the management of damaging activities within a discrete spatial area, and can be effective at reducing the associated impacts, including habitat loss and over-exploitation. Such sites, however, may be exposed to the potential impacts from broader scale pressures, such as anthropogenic litter, due to its diffuse nature and lack of constraint by legislative and/or political boundaries. Plastic, a large component of litter, is of particular concern, due to increasing evidence of its potential to cause ecological and socio-economic damage. The presence of sensitive marine features may mean that some MPAs are at greater potential risk from the impacts of plastic pollution than some non-protected sites. Understanding the abundance, distribution and composition of litter along coastlines is important for designing and implementing effective management strategies. Gathering such data, however, can be expensive and time-consuming but litter survey programmes that enlist citizen scientists are often able to resolve many of the logistical or financial constraints. Here, we examine data collected over 25-years (1994–2018), by Marine Conservation Society volunteers, for spatial patterns in relation to the English MPA network, with the aim of highlighting key sources of litter and identifying management priority areas. We found that MPAs in southeast (Kent) and southwest (Cornwall and Devon) England have the highest densities of shore-based litter. Plastic is the main material constituent and public littering the most common identifiable source. Items attributed to fishing activities were most prevalent in southwest MPAs and sewage related debris was highest in MPAs near large rivers and estuaries, indicating localised accumulation. When comparing inside and outside of MPAs, we found no difference in litter density, demonstrating the need for wider policy intervention at local, national and international scales to reduce the amount of litter.

Information on exposure levels to organochlorine compounds (OCs) in child population is limited, despite their greater vulnerability to the adverse health effects of these chemicals.To determine serum concentrations of 10 OCs (including organochlorine pesticides and polychlorinated biphenyls –PCBs–) in children living in agricultural communities from Almería (South-Eastern Spain), and to identify the main predictors of exposure related to socio-economic characteristics, diet and lifestyle.A cross-sectional study was conducted on 133 children aged 6–11 years selected from public schools of the study area. OCs compounds were determined in serum samples by GC/ECD. Anthropometric measures were obtained during sample collection. Information on sociodemographic characteristics, parental occupation, residential history, lifestyle and frequency of food consumption, among other relevant factors, was obtained by questionnaires administered to the mothers.Geometric means of serum concentrations (ng/ml) were 0.11 for β-hexachloro-cyclohexane (β-HCH), 0.09 for endosulfan, 0.20 for endosulfan-ether, 0.51 for hexachorobenzene (HCB), 0.08 for mirex, 0.06 for oxychlordane, 0.36 for p,p'-DDE, 0.20 for PCB 138, 0.36 for PCB 153, and 0.45 for PCB 180. Percentage of samples above the limit of detection (0.05 ppb) ranged from 32 (β-HCH) to 100 (HCB). A high variability in OC levels depending on the compound was observed between our results and others found in similar studies carried out in children. Variables related to fish consumption were found to be the major dietary determinant of PCB 138, p,p´-DDE, endosulfan-α, β-HCH, mirex and oxychlordane levels.Children participating in this study showed detectable levels of many OC, despite these compounds are no longer used. Their presence in children serum can be explained by their high lipophilicity and environmental persistence, leading to contamination of fatty food. In this line, fish consumption seemed to be the most relevant determinant of OC levels found in our study.

Urban heat island (UHI) can generate profound impacts on socioeconomics, human life, and the environment. Most previous studies have estimated UHI intensity using outdated urban extent maps to define urban and its surrounding areas, and the impacts of urban boundary expansion have never been quantified. Here, we assess the possible biases in UHI intensity estimates induced by outdated urban boundary maps using MODIS Land surface temperature (LST) data from 2009 to 2011 for China's 32 major cities, in combination with the urban boundaries generated from urban extent maps of the years 2000, 2005 and 2010. Our results suggest that it is critical to use concurrent urban extent and LST maps to estimate UHI at the city and national levels. Specific definition of UHI matters for the direction and magnitude of potential biases in estimating UHI intensity using outdated urban extent maps.

Rapid urbanization and the aggregation of human activities in cities have resulted in large amounts of anthropogenic heat (AH) emission, which affects urban climate. Quantifying and assessing the AH emission values accurately and analyzing their spatial distribution characteristics is important to understand the energy exchange processes of urban areas. In this study, the high spatial resolution anthropogenic heat flux (AHF) quantification and spatial distribution analysis were conducted using multi-source data in the Beijing-Tianjin-Hebei region (BTH region) of China. First, the AH emission in district and city level were estimated using inventory method based on energy consumption and socio-economic statistical data; Then, AHF spatial quantification models were constructed based on high spatial resolution nighttime light (NTL) data and Point of interests (POI) data, and 130 m × 130 m gridded AHF quantification result in BTH region was realized; Finally, the potential numerical and spatial distribution patterns of AHF were analyzed using various indicators including contribution rate and aggregation index. The results show that: (1) The parameterized index constructed based on NTL and POI data shows a strong correlation with AHF, with R² ranging from 0.79 to 0.94 and a mean absolute error (MAE) value of 0.72 w·m⁻², which can be applied to the quantification of gridded AHF values with high resolution. The highest total AHF in the study area is 214 w·m⁻², and the average value is 2.24 w·m⁻². (2) Considering the sources of AHF, industrial emission sources in BTH region contribute the most to the total AHF, but commercial building emission sources in Beijing have a higher contribution, which can reach 33.8%. (3) Different types of AHF have different spatial aggregation levels. Commercial building emission and human metabolic emission have the highest aggregation level, and transportation emission has the lowest aggregation level.

Biomass burning, a recurring global phenomenon is also considered an environmental menace, making headlines every year in India with onset of autumn months. Agriculture is demographically the broadest economic sector and plays a significant role in the overall socio-economic fabric of India. Hence, disposal of crop residue is done mainly by burning leading to deterioration of air quality. Residue burning in parts of India is blamed for changing air quality in nearby cities. The spatial distribution of these emissions has always been a challenge due to various data constraints. We hereby present a comprehensive spatially resolved seasonal high resolution gridded (∼10 km × ∼10 km) emission inventory of major pollutants from crop residue burning source in India for the latest year 2018. The winter months contributes almost around ∼50% of total emission followed by summer (∼48%), which is the prime cause of changing air quality in nearby cities. Among all the crops; rice, wheat, maize and sugarcane accounts ∼90% of total PM₁₀ load in the country. The estimated emission for PM₂.₅, PM₁₀, BC and OC, CO, NOx, SO₂, VOC, CH₄ and CO₂ are found to 990.68 Gg/yr, 1231.26 Gg/yr, 123.33 Gg/yr, 410.99 Gg/yr, 11208.18 Gg/yr, 484.55 Gg/yr, 144.66 Gg/yr, 1282.95 Gg/yr, 785.56 Gg/yr and 262051.06 Gg/yr respectively. The cropping pattern and its role in different geographic regions are analysed to identify all potential emission hotspots regions scattered across the country. The developed gridded emissions inventory is envisaged to serve as an important input to regional atmospheric chemistry transport model to better quantify its contribution in deteriorating air quality in various regions of India, paving the way to policy makers to better plan the mitigation and control strategies. The developed fundamental tool is likely to be useful for air quality management.

This study aimed to determine if there is a co-elevation of human blood arsenic and mercury levels in the Midwestern population of the United States (U.S.) and to determine any geographical patterns and variation of arsenic and mercury that may exist in Michigan. 58,800 blood specimens along with associated demographic/geographic data from the contiguous United States were reviewed. Univariate and multivariable logistic regression were used to analyze demographic/geographic variables associated with elevated arsenic concentrations. Furthermore, blood data from patients in Michigan were aggregated to the ZIP code tabulation area (ZCTA) in order to assess geographic variation using spatial regression models. SaTScan software was also used to analyze potential clustering of arsenic and mercury across Michigan ZCTAs. Within the contiguous United States, elevated mercury blood concentrations, older age, female sex, and coastal status were all associated with elevated arsenic blood concentrations (elevated mercury odds ratio (OR) 3.18 (3.04–3.33); female sex OR 1.06 (1.02–1.11); +10 yr age OR 1.12 (1.11–1.14); coastal state OR 1.33 (1.27–1.40). Within the state of Michigan, as with the continuous U.S., elevated mercury blood concentrations and older age were associated with elevated arsenic blood concentrations (elevated mercury OR 2.75 (2.38–3.18); female sex OR 1.06 (0.95–1.19); +10 yr age OR 1.10 (1.06–1.13). Using spatial regression, it was determined that within Michigan, economic inequality (measured via the Gini coefficient) was also associated with elevated concentrations of mercury in the blood. Clinical reference laboratory data, in conjunction with spatial analysis methods, may enhance our understanding of how elemental exposure affects human health and should be considered for studying how environmental contaminants, socioeconomics and geography affect the health of populations.

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Ambient air pollution has been associated with adverse respiratory outcomes, especially among children with asthma. This study reports on associations between daily ambient air pollutant concentrations and the respiratory symptoms of schoolchildren living in Durban, South Africa. This city is Africa's busiest port and a key hub for imported crude oil and exported refined petroleum and petrochemical products, and it experiences a mixture of air pollutants that reflects emissions from industry, traffic and biomass burning. Children in four communities in the highly industrialized southern portion of the city were compared to children of similar socio-economic profiles living in the north of the city. One school was selected in each community. A total of 423 children were recruited. Symptom logs were completed every 1.5–2 h over 3-week period in each of four seasons. Ambient concentrations of NO₂, NO, SO₂, CO, O₃, PM₂.₅ and PM₁₀ were measured throughout the study. Generalized estimating equation (GEE) models were used to estimate odds ratios (ORs) and assess lag effects (1–5 days) using single pollutant (single lags or distributed lags) models. Concentrations of SO₂ and NOₓ were markedly higher in the south, while PM₁₀ did not vary. Significant increase in the odds ratios of cough were identified for the various lags analyzed. The OR of symptoms was further increased among those living in the south compared to the north. In conclusion, in this analysis of over 70,000 observations, we provide further evidence that exposure to PM₁₀, SO₂, NO₂ and NO is associated with significantly increased occurrence of respiratory symptoms among children. This was evident for cough, shortness of breath, and chest tightness, across the four pollutants and for different lags of exposure. This is the first study describing these changes in sub-Saharan Africa.

A new Land Use Regression model was built to develop pan-European 100 m resolution maps of NO2 concentrations. The model was built using NO2 concentrations from routine monitoring stations available in the Airbase database as dependent variable. Predictor variables included land use, road traffic proxies, population density, climatic and topographical variables, and distance to sea. In order to capture international and inter regional disparities not accounted for with the mentioned predictor variables, additional proxies of NO2 concentrations, like levels of activity intensity and NOx emissions for specific sectors, were also included. The model was built using Random Forest techniques. Model performance was relatively good given the EU-wide scale (R2 = 0.53). Output predictions of annual average concentrations of NO2 were in line with other existing models in terms of spatial distribution and values of concentration. The model was validated for year 2015, comparing model predictions derived from updated values of independent variables, with concentrations in monitoring stations for that year. The algorithm was then used to model future concentrations up to the year 2030, considering different emission scenarios as well as changes in land use, population distribution and economic factors assuming the most likely socio-economic trends. Levels of exposure were derived from maps of concentration. The model proved to be a useful tool for the ex-ante evaluation of specific air pollution mitigation measures, and more broadly, for impact assessment of EU policies on territorial development.