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.

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.

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health.

Significant volumes of research over the past four decades has sought to elucidate the social, infrastructural, economic, and human health effects of climate change induced surface flooding. To date, epidemiological and public health studies of flooding events have focused on mental health effects, vector-borne diseases, and infectious enteric disease due to floodwater contact (i.e. typically low consumption rates). The inherent nature of groundwater (i.e. out of sight, out of mind) and the widely held belief that aquifers represent a pristine source of drinking water due to natural attenuation may represent the “perfect storm” causing direct consumption of relatively large volumes of surface flood-contaminated groundwater. Accordingly, the current study sought to systematically identify and synthesize all available peer-reviewed literature pertaining to the nexus between surface flooding, groundwater contamination and human gastroenteric outcomes. Just 14 relevant studies were found to have been published during the period 1980–2017, thus highlighting the fact that this potentially significant source of climate-related exposure to environmental infection has remained understudied to date. Studies differed significantly in terms of type and data reporting procedures, making it difficult to discern clear trends and patterns. Approximately 945 confirmed cases of flood-related enteric disease were examined across studies; these concurred with almost 10,000 suspected cases, equating to approximately 20 suspected cases per confirmed case. As such, no regional, national or global estimates are available for the human gastrointestinal health burden of flood-related groundwater contamination. In light of the demonstrable public health significance of the concurrent impacts of groundwater susceptibility and climate change exacerbation, strategies to increase awareness about potential sources of contamination and motivate precautionary behaviour (e.g. drinking water testing and treatment, supply interruptions) are necessary. Mainstreaming climate adaptation concerns into planning policies will also be necessary to reduce human exposure to waterborne sources of enteric infection.

The aim of this study was to investigate the key landscape structures of migratory bird habitats that affect abundance of migratory birds to promote resilient coastal green infrastructure planning on the Yellow Sea coast. We classified coastal areas into four watersheds of South Korea and conducted multivariate regression analysis between migratory bird populations and landscape structures including total class area (CA), patch area distribution (MN), patch density (PD), and edge density (ED). At the national level, sandbank MN, sandbank CA, water ED, and grasslands were derived as key landscape structures affecting the abundance of migratory birds. At the watershed level, key landscape structures were determined as follows: Urban area_MN for the Han River watershed, rice paddy MN for the Asan watershed, rice paddy CA for Saemangeum, and grassland MN for the Youngsan River watershed. Considering the multifunctionality, redundancy, and connectivity of the resilience strategy, we provide specific coastal infrastructure planning recommendations at the national and watershed scales.

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.

Parks with various types of vegetations played an important role in ameliorating air quality in urban areas. However, the attenuation effect of urban vegetation on levels of air pollution was rarely been experimentally estimated. This study, using seasonal monitoring data of total suspended particles (TSP), sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) from six parks in Pudong District, Shanghai, China, demonstrated vegetations in parks can remove large amount of airborne pollutants. In addition, crown volume coverage (CVC) was introduced to characterize vegetation conditions in parks and a mixed-effects model indicated that CVC and the pollution diffusion distance were key predictors influencing pollutants removal rate. Therefore, it could be estimated by regression analysis that in summer, urban vegetations in Pudong District could contribute to 9.1% of TSP removal, 5.3% of SO₂ and 2.6% of NO₂. The results could be considered for a better park planning and improving air quality.

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.