Global urbanisation has resulted in population densification, which is associated with increased air pollution, mainly from anthropogenic sources. One of the systems proposed to mitigate urban air pollution is urban forestry. This study quantified the spatial associations between concentrations of CO, NO₂, SO₂, and PM₁₀ and urban forestry, whilst correcting for anthropogenic sources and sinks, thus explicitly testing the hypothesis that urban forestry is spatially associated with reduced air pollution on a city scale. A Land Use Regression (LUR) model was constructed by combining air pollutant concentrations with environmental variables, such as land cover type and use, to develop predictive models for air pollutant concentrations. Traffic density and industrial air pollutant emissions were added to the model as covariables to permit testing of the main effects after correcting for these air pollutant sources. It was found that the concentrations of all air pollutants were negatively correlated with tree canopy cover and positively correlated with dwelling density, population density and traffic count. The LUR models enabled the establishment of a statistically significant spatial relationship between urban forestry and air pollution mitigation. These findings further demonstrate the spatial relationships between urban forestry and reduced air pollution on a city-wide scale, and could be of value in developing planning policies focused on urban greening.

Previous studies reported triggering of acute cardiovascular events by short-term increasedPM₂.₅ concentrations. From 2007 to 2013, national and New York state air quality policies and economic influences resulted in reduced concentrations of PM₂.₅ and other pollutants across the state. We estimated the rate of cardiovascular hospital admissions associated with increased PM₂.₅ concentrations in the previous 1–7 days, and evaluated whether they differed before (2005–2007), during (2008–2013), and after these concentration changes (2014–2016).Using the Statewide Planning and Research Cooperative System (SPARCS) database, we retained all hospital admissions with a primary diagnosis of nine cardiovascular disease (CVD) subtypes, for residents living within 15 miles of PM₂.₅ monitoring sites in Buffalo, Rochester, Albany, Queens, Bronx, and Manhattan from 2005 to 2016 (N = 1,922,918). We used a case-crossover design and conditional logistic regression to estimate the admission rate for total CVD, and nine specific subtypes, associated with increased PM₂.₅ concentrations.Interquartile range (IQR) increases in PM₂.₅ on the same and previous 6 days were associated with 0.6%–1.2% increases in CVD admission rate (2005–2016). There were similar patterns for cardiac arrhythmia, ischemic stroke, congestive heart failure, ischemic heart disease (IHD), and myocardial infarction (MI). Ambient PM₂.₅ concentrations and annual total CVD admission rates decreased across the period. However, the excess rate of IHD admissions associated with each IQR increase in PM₂.₅ in previous 2 days was larger in the after period (2.8%; 95%CI = 1.5%–4.0%) than in the during (0.6%; 95%CI = 0.0%–1.2%) or before periods (0.8%; 95%CI = 0.2%–1.3%), with similar patterns for total CVD and MI, but not other subtypes.While pollutant concentrations and CVD admission rates decreased after emission changes, the same PM₂.₅ mass was associated with a higher rate of ischemic heart disease events. Future work should confirm these findings in another population, and investigate whether specific PM components and/or sources trigger IHD events.

Globally, millions of people who rely on groundwater for potable purposes and agriculture have been inadvertently exposed to toxic arsenic (As) because of its natural occurrence in groundwater in several countries of Asia, Europe and America. While the presence of As in groundwater and its impacts on human health have been documented in many countries, there is little information on As contamination in Pakistan. This review highlights, for the first time, the extent and severity of As-induced problems in Pakistan based on relevant published papers; discusses possible sources of As contamination of aquifers; and estimates As-induced potential health hazards in the country in relation to global data. Data from 43 studies (>9882 groundwater samples) were used to describe As variability in groundwater of Pakistan and for comparison with global data. The mean groundwater As content reported in these studies was 120 μg/L (range: 0.1–2090 μg/L; SD: ±307). About 73% of the values for mean As contents in the 43 studies were higher than the World Health Organization (WHO) permissible limit (10 μg/L) for drinking water, while 41% were higher than the permissible limit of As in Pakistan (50 μg/L). It was observed that groundwater samples in some areas of Punjab and Sindh provinces contained high As concentrations which were almost equal to concentrations reported in the most contaminated areas of the world. We predicted that the mean values of ADD, HQ and CR were 4.4 μg kg⁻¹day⁻¹ (range: 0–77 μg kg⁻¹day⁻¹), 14.7 (range: 0–256) and 0.0029 (range: 0–0.0512), respectively, based on mean As concentrations reported in Pakistan. In addition, this article proposes some integrated sustainable solutions and future perspectives keeping in view the regional and global context, as well as the on-ground reality of the population drinking As-contaminated water, planning issues, awareness among civil society and role of the government bodies. Based on available data, it is predicted that almost 47 million people in Pakistan are residing in areas where more than 50% of groundwater wells contain As concentrations above the WHO recommended limit of As in drinking water.

Unmanned aerial vehicles (UAVs) offer new opportunities to monitor pollution and provide valuable information to support remediation. Their low-cost, ease of use, and rapid deployment capability make them ideal for environmental emergency response. Here we present a UAV-based study of the third largest coal ash spill in the United States. Coal ash from coal combustion is a toxic industrial waste material present worldwide. Typically stored in settling ponds in close proximity to waterways, coal ash poses significant risk to the environment and drinking water supplies from both chronic contamination of surface and ground water and catastrophic pond failure. We sought to provide an independent estimate of the volume of coal ash and contaminated water lost during the rupture of the primary coal ash pond at the Dan River Steam Station in Eden, NC, USA and to demonstrate the feasibility of using UAVs to rapidly respond to and measure the volume of spills from ponds or containers that are open to the air. Using structure-from-motion (SfM) imagery analysis techniques, we reconstructed the 3D structure of the pond bottom after the spill, used historical imagery to estimate the pre-spill waterline, and calculated the volume of material lost. We estimated a loss of 66,245 ± 5678 m3 of ash and contaminated water. The technique used here allows rapid response to environmental emergencies and quantification of their impacts at low cost, and these capabilities will make UAVs a central tool in environmental planning, monitoring, and disaster response.

Urban metabolism refers to the assessment of the amount of resources produced and consumed by urban ecosystems. It has become an important tool to understand how the development of one city causes impacts to the local and regional environment and to support a more sustainable urban design and planning. Therefore, the purpose of this paper was to measure the changes in material and energy use occurred in the city of Curitiba (Brazil) between the years of 2000 and 2010. Results reveal better living conditions and socioeconomic improvements derived from higher resource throughput but without complete disregard to environmental issues. Food intake, water consumption and air emissions remained at similar levels; energy use, construction materials and recycled waste were increased. The paper helps illustrate why it seems more adequate to assess the contribution a city makes to sustainable development than to evaluate if one single city is sustainable or not.

We examined the consequences of the spatial heterogeneity of atmospheric ammonia (NH3) by measuring and modelling NH3 concentrations and deposition at 25 m grid resolution for a rural landscape containing intensive poultry farming, agricultural grassland, woodland and moorland. The emission pattern gave rise to a high spatial variability of modelled mean annual NH3 concentrations and dry deposition. Largest impacts were predicted for woodland patches located within the agricultural area, while larger moorland areas were at low risk, due to atmospheric dispersion, prevailing wind direction and low NH3 background. These high resolution spatial details are lost in national scale estimates at 1 km resolution due to less detailed emission input maps. The results demonstrate how the spatial arrangement of sources and sinks is critical to defining the NH3 risk to semi-natural ecosystems. These spatial relationships provide the foundation for local spatial planning approaches to reduce environmental impacts of atmospheric NH3.

Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The use of climatic maps, such Urban Climate Zone‑UCZ, is adapted for this kind of application. The objective of this paper is to demonstrate that the UCZ classification, integrated in the World Meteorological Organization guidelines, first can be automatically determined for sample areas and second is meaningful according to climatic variables. The analysis presented is applied on Toulouse urban area (France). Results show first that UCZ differentiate according to air and surface temperature. It has been possible to determine the membership of sample areas to an UCZ using landscape descriptors automatically computed with GIS and remote sensed data. It also emphasizes that climate behavior and magnitude of UCZ may vary from winter to summer. Finally we discuss the influence of climate data and scale of observation on UCZ mapping and climate characterization.

Human factors/errors (such as inappropriate actions by operators and unsafe supervision by organizations) are a primary cause of oil spill incidents. To investigate the influences of active operational failures and unsafe latent factors in offshore oil spill accidents, an integrated human factor analysis and decision support process has been developed. The system is comprised of a Human Factors Analysis and Classification System (HFACS) framework to qualitatively evaluate the influence of various factors and errors associated with the multiple operational stages considered for oil spill preparedness and response (e.g., oil spill occurrence, spill monitoring, decision making/contingency planning, and spill response); coupled with quantitative data analysis by Fuzzy Set Theory and the Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) to enhance decision making during response operations. The efficiency of the integrated human factor analysis and decision support system is tested with data from a case study to generate a comprehensive priority rank, a robust sensitivity analysis, and other theoretical/practical insights. The proposed approach improves our knowledge on the significance of human factors/errors on oil spill accidents and response operations; and provides an improved support tool for decision making.

Nitrate (NO₃⁻) is a key component of secondary inorganic aerosols and PM₂.₅. However, the contributions of nitrogen oxides (NOₓ) emission sources to NO₃⁻ in PM₂.₅ remain poorly constrained. This study measured nitrogen (N) isotopes of NO₃⁻ (hereafter as δ¹⁵N-NO₃⁻) in PM₂.₅ collected at Beijing in 2014. We observed that δ¹⁵N-NO₃⁻ values in PM₂.₅ (−2.3‰ − 19.7‰; 7.3 ± 5.4‰ annually) were significantly higher in winter (11.9 ± 4.4‰) than in summer (2.2 ± 2.5‰). The δ¹⁵N differences between source NOₓ and NO₃⁻ in PM₂.₅ (hereafter as Δ values) were estimated by a computation module as 7.8 ± 2.2‰ − 10.4 ± 1.6‰ (8.8 ± 2.4‰). Using the Δ values and δ¹⁵N values of NOₓ from major fossil (coal combustion, vehicle exhausts) and non-fossil sources (biomass burning, microbial N cycle), contributions of major NOₓ sources to NO₃⁻ in PM₂.₅ were further estimated by the SIAR model. We found that seasonal variations of δ¹⁵N-NO₃⁻ values in PM₂.₅ of Beijing were mainly caused by those of NOₓ contributions from coal combustion (38 ± 10% in winter, 20 ± 9% in summer). Annually, NOₓ from coal combustion, vehicle exhausts, biomass burning, and microbial N cycle contributed 28 ± 12%, 29 ± 17%, 27 ± 15%, and 16 ± 7% to NO₃⁻ in PM₂.₅, respectively, showing actually comparable contributions between non-fossil NOₓ (43 ± 16%) and fossil NOₓ (57 ± 21%). These results are useful for planning the reduction of NOₓ emissions in city environments and for elucidating relationships between regional NOₓ emissions and atmospheric NO₃⁻ pollution or deposition.

It is a great challenge to identify the many and varied sources of soil heavy metal pollution. Often little information is available regarding the anthropogenic factors and enterprises that could potentially pollute soils. In this study we use freely available geographical data from a search engine in conjunction with machine learning methodologies to identify and classify potentially polluting enterprises in the Yangtze Delta, China. The data were classified into 31 separate and four integrated industry types by five different machine learning approaches. Multinomial naive Bayesian (NB) methods achieved an accuracy of 87% and Kappa coefficient of 0.82 and were used to classify the geographic data from more than 260,000 enterprises. The relationship between the different industry classes and measurements of soil cadmium (Cd) and mercury (Hg) concentrations was explored using bivariate local Moran's I analysis. The analysis revealed areas where different industry classes had led to soil pollution. In the case of Cd, elevated concentrations also occurred in some areas because of excessive fertilization and coal mining. This study provides a new approach to investigate the interaction between anthropogenic pollution and natural sources of soil heavy metals to inform pollution control and planning decisions regarding the location of industrial sites.