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.

Ambient particulate pollution, especially PM₂.₅, has adverse impacts on health and welfare. To manage and control PM₂.₅ pollution, it is of great importance to determine the factors that affect PM₂.₅ levels. Previous studies commonly focused on a single or several cities. This study aims to analyze the impacts of meteorological and socio-economic factors on daily concentrations of PM₂.₅ in 109 Chinese cities from January 1, 2015 to December 31, 2015.To evaluate potential risk factors associated with the spatial and temporal variations in PM₂.₅ levels, we developed a Bayesian spatio-temporal model in which the potential temporal autocorrelation and spatial autocorrelation of PM₂.₅ levels were taken into account to ensure the independence of the error term of the model and hence the robustness of the estimated parameters.Daily concentrations of PM₂.₅ peaked in winter and troughed in summer. The annual average concentration reached its highest value (79 μg/m³) in the Beijing-Tianjin-Hebei area. The city-level PM₂.₅ was positively associated with the proportion of the secondary industry, the total consumption of liquefied petroleum gas and the total emissions of industrial sulfur dioxide (SO₂), but negatively associated with the proportion of the primary industry. A reverse U-shaped relationship between population density and PM₂.₅ was found. The city-level and daily-level of weather conditions within a city were both associated with PM₂.₅.PM₂.₅ levels had significant spatio-temporal variations which were associated with socioeconomic and meteorological factors. Particularly, economic structure was a determinant factor of PM₂.₅ pollution rather than per capita GDP. This finding will be helpful for the intervention planning of particulate pollution control when considering the environmental and social-economic factors as part of the strategies.

Agricultural soils are a leading source of atmospheric greenhouse gas (GHG) emissions and are major contributors to global climate change. Carbon dioxide (CO2) makes up 20% of the total GHG emitted from agricultural soil. Therefore, an evaluation of CO2 emissions from agricultural soil is necessary in order to make mitigation strategies for environmental efficiency and economic planning possible. However, quantification of CO2 emissions through experimental methods is constrained due to the large time and labour requirements for analysis. Therefore, a modelling approach is needed to achieve this objective. In this paper, the DeNitrification-DeComposition (DNDC), a process-based model, was modified to predict CO2 emissions for Canada from regional conditions. The modified DNDC model was applied at three experimental sites in the province of Saskatchewan. The results indicate that the simulations of the modified DNDC model are in good agreement with observations. The agricultural management of fertilization and irrigation were evaluated using scenario analysis. The simulated total annual CO2 flux changed on average by ±13% and ±1% following a ±50% variance of the total amount of N applied by fertilising and the total amount of water through irrigation applications, respectively. Therefore, careful management of irrigation and applications of fertiliser can help to reduce CO2 emissions from the agricultural sector.

This paper presents a performance evaluation framework for streetscape vegetation. A performance index (PI) is conceived using the following seven traits, specific to the street environments – Pollution Flux Potential (PFP), Carbon Sequestration Potential (CSP), Thermal Comfort Potential (TCP), Noise Attenuation Potential (NAP), Biomass Energy Potential (BEP), Environmental Stress Tolerance (EST) and Crown Projection Factor (CPF). Its application is demonstrated through a case study using fifteen street vegetation species from the UK, utilising a combination of direct field measurements and inventoried literature data. Our results indicate greater preference to small-to-medium size trees and evergreen shrubs over larger trees for streetscaping. The proposed PI approach can be potentially applied two-fold: one, for evaluation of the performance of the existing street vegetation, facilitating the prospects for further improving them through management strategies and better species selection; two, for planning new streetscapes and multi-functional biomass as part of extending the green urban infrastructure.

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.

Size-segregated particulate pollutants (PM<0.95, PM0.95–1.5, PM1.5–3.0, PM3.0–7.2 and PM>7.2) were collected over Patiala (30.33°N, 76.40°E; 250 m amsl), a semi-urban city located in northwestern Indo-Gangetic Plain (IGP), during October, 2012 to September, 2013. Mass concentration of total suspended particulates (TSP), derived by summation of particulate (aerosol) mass in different size range, varied from 88 to 387 μg m−3 with highest mass concentration (∼55% of total mass) in submicron size (PM<0.95) during the entire study period, which broadly reflects relative higher contribution of various anthropogenic sources (emissions from biomass and bio-fuel burning, vehicles, thermal power plants, etc) to ambient particles. Concentration of SO42−, NO3−, NH4+, K+ and Ca2+ exhibited large variability ranging from 0.52 to 40, 0.20 to 19, 0.14 to 12, 0.06 to 5.3 and 0.08 to 5.6 μg m−3, respectively, in different size ranges with varying size distribution for most of the species, except NH4+. A strong linear correlation (r = 0.97) between (SO42− + NO3−) and (K+ + NH4+) concentrations has been observed in submicron particles collected in different seasons, suggesting the formation of secondary inorganic salts. However, relatively poor correlation is observed in higher size ranges where significant correlation between (SO42− + NO3−) and (Ca2+ + Mg2+) has been observed. These observations indicate the acid neutralization by dust in coarser modes of particles. Chemical composition of submicron particulates (PM<0.95) in different seasons as well as for whole year was used to identify PM sources through the application of Positive Matrix Factorization (PMF, version 5.0) model. Based on annual data, PMF analyses suggests that six source factors namely biomass burning emission (24%), vehicular emission (22%), secondary organic aerosols (20%), power plant emission (13%), secondary inorganic aerosols (12%) and mineral dust (9%) contribute to PM<0.95 loading over the study region. Such studies are important in dispersion modeling, health impact assessment, and planning of pollution mitigation strategies.

Air pollution has been shown to be significantly associated with morbidity and mortality in urban areas, but there is lack of studies focused on extreme pollution events such as extreme dust episodes in high-density Asian cities. However, such cities have had extreme climate episodes that could have adverse health implications for downwind areas. More importantly, few studies have comprehensively investigated the mortality risks of extreme dust events for socioeconomically vulnerable populations.This paper examined the association between air pollutants and mortality risk in Hong Kong from 2006 to 2010, with a case-crossover analysis, to determine the elevated risk after an extreme dust event in a high-density city. The results indicate that PM₁₀₋₂.₅ dominated the all-cause mortality effect at the lag 0 day (OR: 1.074 [1.051, 1.098]). This study also found that people who were aged ≥ 65, economically inactive, or non-married had higher risks of all-cause mortality and cardiorespiratory mortality during days with extreme dust events. In addition, people who were in areas with higher air pollution had significantly higher risks of all-cause mortality and cardiorespiratory mortality.In conclusion, the results of this study can be used to target the vulnerable among a population or an area and the day(s) at risk to assist in health protocol development and emergency planning, as well as to develop early warnings for the general public in order to mitigate potential mortality risk for vulnerable population groups caused by extreme dust events.

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.

Coastal areas provide important ecosystem services and affect local tourism. However, these areas are also sensitive to coastal erosion. The purpose of this study was to simulate a landscape plan scenario to improve the value of ecosystem services. The Shinduri coastal area in South Korea which has important natural resources, such as coastal sand dunes and coastal forests. To simulate landscape changes, this study was conducted using system dynamics. The study progressed in three stages: first, an analysis of the landscape change behavior model of Shinduri in its current state and an evaluation of the value of ecosystem services was conducted. Second, a simulation was carried out by applying a coastal erosion scenario. Third, a simulation of landscape change was run, and the value of ecosystem services was estimated, with regard to afforestation, thinning, weeding and coastal sand dune restoration plan scenarios. The results were as follows: in the absence of disturbances, current landscape change models are stable, and the value of ecosystem services, which was $859,259 in 2014, has increased over time. However, the value of ecosystem services decreased when subjected to a coastal erosion scenario. The evaluation of value of ecosystem services under afforestation, thinning, weeding and coastal sand dune plan scenarios revealed an optimal landscape plan that focuses on a coastal sand dune restoration plan suggesting restoration of these dunes at a rate of 27.05 ha per year. When the coastal sand dune restoration plan is applied, the value of ecosystem services increases to $ 895,474 by 2054. The coastal sand dune restoration plan should prioritize the protection of the coastal sand dune area as component of the restoration of coastal ecological resources in the area. These findings could contribute to the ecological management and improvement of coastal ecosystem services.