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.

This study develops an oil spill environmental vulnerability model for predicting and mapping the oil slick trajectory pattern in Kota Tinggi, Malaysia. The impact of seasonal variations on the vulnerability of the coastal resources to oil spill was modelled by estimating the quantity of coastal resources affected across three climatic seasons (northeast monsoon, southwest monsoon and pre-monsoon). Twelve 100 m³ (10,000 splots) medium oil spill scenarios were simulated using General National Oceanic and Atmospheric Administration Operational Oil Modeling Environment (GNOME) model. The output was integrated with coastal resources, comprising biological, socio-economic and physical shoreline features. Results revealed that the speed of an oil slick (40.8 m per minute) is higher during the pre-monsoon period in a southwestern direction and lower during the northeast monsoon (36.9 m per minute). Evaporation, floating and spreading are the major weathering processes identified in this study, with approximately 70% of the slick reaching the shoreline or remaining in the water column during the first 24 h (h) of the spill. Oil spill impacts were most severe during the southwest monsoon, and physical shoreline resources are the most vulnerable to oil spill in the study area. The study concluded that variation in climatic seasons significantly influence the vulnerability of coastal resources to marine oil spill.

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.

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.

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.

Many regulatory screening level exposure assessments are based on simple large scale conceptual scenarios. However, exposure, and therefore risks associated with chemicals, are characterised by high spatial variability. The Scenario assembly tool (ScenAT) is a global screening level model to enable spatially resolved local predictions of environmental concentrations of home and personal care chemicals. It uses the European Union Technical Guidance Document (TGD) equation to predict local scale freshwater concentrations (predicted environmental concentrations - PECs) of chemicals discharged via wastewater. ScenAT uses Geographic Information System (GIS) layers for the underlying socio-economic (population) and environmental parameters (per capita water use, sewage treatment plant connectivity, dilution factor). Using a probabilistic approach, we incorporate sources of uncertainty in the input data (tonnage estimation, removal in sewage treatment plants and seasonal variability in dilution factors) for two case-study chemicals: the antimicrobial triclosan (TCS) and the anionic surfactant linear alkylbenzene sulphonate (LAS). We then compare model estimates of wastewater and freshwater concentrations of TCS and LAS to UK monitoring data. Comparison showed that modeled PECs were on average higher than mean measured data for TCS and LAS by a factor 1.8 and 1.4, respectively. Considering the uncertainty associated with both model and monitoring data, the use of a probabilistic approach using the ScenAT model for screening assessment is reasonable. The combination of modelled and monitoring data enables the contextualisation of monitoring data. Spatial PECs can be used to identify areas of elevated concentration for further refined assessment.A probabilistic approach for large scale screening assessments to contextualise monitoring data for risk assessment.

The impact of surface ozone pollution on winter wheat yield is empirically estimated by considering socio-economic and weather determinants. This research is the first to use an economic framework to estimate the ozone impact, and a unique county-level panel is employed to examine the impact of the increasing surface ozone concentration on the productivity of winter wheat in China. In general, the increment of surface ozone concentration during the ozone-sensitive period of winter wheat is determined to be harmful to its yield, and a conservative reduction of ozone pollution could significantly increase China's wheat supply.