The state of practice for noise assessment utilizes established standards for emission and propagation modelling of linear and point sources. Recently, land use regression (LUR) modelling has emerged as an alternative method due to relatively low data and computing resource demands. However, a limitation of LUR modelling is that is does not account for noise attenuation and reflections by features of the built environment. This study demonstrates and validates a method that combines the two modelling frameworks to exploit their respective strengths: Emission and propagation based prediction of traffic noise, the predominant source of noise at the level of streetscapes, and a LUR-based correction for noise sources that vary on spatial scales beyond the streetscape.Multi-criteria analysis, location-allocation modelling and stakeholder consultation identified 220 monitoring sites with optimal coverage for a 1-week sampling period. A subset of sites was used to validate a road traffic noise emission and propagation model and to specify a LUR model that predicted the contribution of other sources. The equivalent 24-h sound pressure level (LAeq) for all sites was 62.9 dBA (SD 6.4). This varied by time of day, weekday, types of roads and land uses. The traffic noise emission model demonstrated a high level of covariance with observed noise levels, with R² values of 0.58, 0.60 and 0.59 for daytime, nighttime and 24-h periods, respectively. Combined with LUR models to correct for other noise sources, the hybrid models R² values were 0.64, 0.71 and 0.67 for the respective time periods.The study showed that road traffic noise emissions account for most of the variability of total environmental noise in Toronto. The combined approach to predict fine resolution noise exposures with emission and receptor-based models presents an effective alternative to noise modelling approaches based on emission and propagation or LUR modelling.

Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject's pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies.

Hyperspectral imaging technology has been investigated as a possible way to detect microplastics contamination in soil directly and efficiently in this study. Hyperspectral images with wavelength range between 400 and 1000 nm were obtained from soil samples containing different materials including microplastics, fresh leaves, wilted leaves, rocks and dry branches. Supervised classification algorithms such as support vector machine (SVM), mahalanobis distance (MD) and maximum likelihood (ML) algorithms were used to identify microplastics from the other materials in hyperspectral images. To investigate the effect of particle size and color, white polyethylene (PE) and black PE particles extracted from soil with two different particle size ranges (1–5 mm and 0.5–1 mm) were studied in this work. The results showed that SVM was the most applicable method for detecting white PE in soil, with the precision of 84% and 77% for PE particles in size ranges of 1–5 mm and 0.5–1 mm respectively. The precision of black PE detection achieved by SVM were 58% and 76% for particles of 1–5 mm and 0.5–1 mm respectively. Six kinds of household polymers including drink bottle, bottle cap, rubber, packing bag, clothes hanger and plastic clip were used to validate the developed method, and the classification precision of polymers were obtained from 79% to 100% and 86%–99% for microplastics particle 1–5 mm and 0.5–1 mm respectively. The results indicate that hyperspectral imaging technology is a potential technique to determine and visualize the microplastics with particle size from 0.5 to 5 mm on soil surface directly.

Emissions from the transportation sector are rapidly changing worldwide; however, the interplay of such emission changes in the face of climate change are not as well understood. This two-part study examines the impact of projected emissions from the U.S. transportation sector (Part I) on ambient air quality in the face of climate change (Part II). In Part I of this study, we describe the methodology and results of a novel Technology Driver Model (see graphical abstract) that includes 1) transportation emission projections (including on-road vehicles, non-road engines, aircraft, rail, and ship) derived from a dynamic technology model that accounts for various technology and policy options under an IPCC emission scenario, and 2) the configuration/evaluation of a dynamically downscaled Weather Research and Forecasting/Community Multiscale Air Quality modeling system.By 2046–2050, the annual domain-average transportation emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH3), and sulfur dioxide (SO2) are projected to decrease over the continental U.S. The decreases in gaseous emissions are mainly due to reduced emissions from on-road vehicles and non-road engines, which exhibit spatial and seasonal variations across the U.S. Although particulate matter (PM) emissions widely decrease, some areas in the U.S. experience relatively large increases due to increases in ship emissions. The on-road vehicle emissions dominate the emission changes for CO, NOx, VOC, and NH3, while emissions from both the on-road and non-road modes have strong contributions to PM and SO2 emission changes. The evaluation of the baseline 2005 WRF simulation indicates that annual biases are close to or within the acceptable criteria for meteorological performance in the literature, and there is an overall good agreement in the 2005 CMAQ simulations of chemical variables against both surface and satellite observations.

High arsenic groundwater generally coexists with elevated Fe2+ concentrations (mg L−1 levels) under reducing conditions, but an explanation for the extremely high arsenic (up to ∼2690) concentrations at very low Fe2+ (i.e., μg L−1 levels) in groundwater of Datong Basin remains elusive. Field groundwater investigation and laboratory microcosm experiments were implemented in this study. The field groundwater was characterized by weakly alkaline (pH 7.69 to 8.34) and reducing conditions (Eh −221.7 to −31.9 mV) and arsenic concentration averages at 697 μg L−1. Acinetobacter (5.9–51.3%), Desulfosporosinus (4.6–30.2%), Brevundimonas (3.9–19%) and Pseudomonas (3.2–14.6%) were identified as the dominant genera in the bacterial communities. Bacterially mediated arsenate reduction, Fe(III) reduction, and sulfate reduction are processes occurring (or having previously occurred) in the groundwater. Results from incubation experiment (27 d) revealed that nitrate, arsenate, and Fe(III)/sulfate reduced sequentially with time under anoxic conditions, while Fe(III) and sulfate reduction processes had no obvious differences, occurring almost simultaneously. Moreover, low Fe2+ concentrations were attributed to initially high pH conditions, which relatively retarded Fe(III) reduction. In addition, arsenic behavior in relation to groundwater redox conditions, matrices, and solution chemistry were elaborated. Bacterial arsenate reduction process proceeded before Fe(III) and sulfate reduction in the incubation experiment, and the total arsenic concentration (dominated by arsenite) gradually increased from ∼7 to 115 μg L−1 as arsenate was reduced. Accordingly, bacterially mediated reductive desorption of arsenate is identified as the main process controlling arsenic mobility, while Fe(III) reduction coupled with sulfate reduction are secondary processes that have also contributed to arsenic enrichment in the study site. Overall, this study provide important insights into the mechanism controlling arsenic mobility under weakly alkaline and reducing conditions, and furnishes that arsenate reduction by bacteria play a major role leading to high accumulation of desorbed arsenite in groundwater.

The widespread application of organophosphorous flame retardants (OPFRs) has led to considerable human exposure, with major concerns regarding their health risks. Herein, we investigate the effects of triphenyl phosphate (TPP), one of the most widely used OPFRs, and one of its main metabolite diphenyl phosphate (DPP) on the endocrine systems and metabolic profiles after neonatal exposure from postnatal days 1–10 at two dosages (2 and 200 μg per day). Both TPP and DPP had no negative effect on uterine weight, glucose tolerance, and estradiol. 1H-NMR-based metabolomics revealed a sex-specific metabolic disturbance of TPP. Specifically, low dose of TPP altered the metabolic profiles of male mice while exerting no significant effects on female ones. Furthermore, a dose-dependent effect of TPP in male mice was observed, where a low toxicity dose up-regulated lipid-related metabolites, while a high toxicity dose down-regulated the pyruvate metabolism and TCA cycles. These results highlight the importance of carefully assessing the health impact of TPP on infants.

Maternal arsenic exposure leads to adverse birth outcomes, but the critical window of this susceptibility keeps unclear. To determine whether the associations between maternal arsenic exposure and birth outcomes were trimester-specific, we conducted a birth cohort study of 1390 women from 2014 to 2016 in Wuhan, China. We examined associations between total urinary arsenic concentrations in three trimesters and birth weight, birth length and the risk of small for gestational age (SGA), and the differences of these associations across trimesters using generalized estimating equations. Maternal urinary arsenic concentrations varied across trimesters and were weakly correlated. Arsenic concentrations in the 3rd trimester, but not in the 1st and 2nd trimesters, were associated with birth outcomes. For each doubling of arsenic levels in the 3rd trimester, birth weight was decreased 24.27 g (95% confidence interval (CI): −46.99, −1.55), birth length was decreased 0.13 cm (95% CI: −0.22, −0.04), and the risk for SGA birth was increased 25% (95% CI: 1.03, 1.49). Further, stratified analyses indicated that these associations were only observed in female infants. Our findings indicate maternal arsenic levels in the 3rd trimester seemed to have significant impacts on birth outcomes, and also emphasize the public health interventions relevance to arsenic exposure in late pregnancy.

PM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a ‘high-value increasing and low-value decreasing’ trend. Areas with annual PM2.5 concentrations of more than 70μg/m3 and less than 10μg/m3 expanded, while areas with of an annual PM2.5 concentrations of 10–25μg/m3 shrank. The most heavily PM2.5-polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5-polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5-polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5-polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks.

Soil contamination due to atmospheric deposition of metals originating from smelters is a global environmental problem. A common problem associated with this contamination is the discrimination between anthropic and natural contributions to soil metal concentrations: In this context, we investigated the characteristics of soil contamination in the surrounding area of a world class smelter. We attempted to combine several approaches in order to identify sources of metals in soils and to examine contamination characteristics, such as pollution level, range, and spatial distribution. Soil samples were collected at 100 sites during a field survey and total concentrations of As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, and Zn were analyzed. We conducted a multivariate statistical analysis, and also examined the spatial distribution by 1) identifying the horizontal variation of metals according to particular wind directions and distance from the smelter and 2) drawing a distribution map by means of a GIS tool. As, Cd, Cu, Hg, Pb, and Zn in the soil were found to originate from smelter emissions, and As also originated from other sources such as abandoned mines and waste landfill. Among anthropogenic metals, the horizontal distribution of Cd, Hg, Pb, and Zn according to the downwind direction and distance from the smelter showed a typical feature of atmospheric deposition (regression model: y = y0 + αe−βx). Lithogenic Fe was used as an indicator, and it revealed the continuous input and accumulation of these four elements in the surrounding soils. Our approach was effective in clearly identifying the sources of metals and analyzing their contamination characteristics. We believe this study will provide useful information to future studies on soil pollution by metals around smelters.

The widespread cultivation of transgenic Bt cotton makes assessing the potential effects of this recombinant crop on non-target organisms a priority. However, the effect of Bt cotton on many insects is currently virtually unknown. The plant bug Adelphocoris suturalis is now a major pest of cotton in southern China and the beetle Haptoncus luteolus is one of the most ancient cotton pollinators. We conducted laboratory experiments to evaluate the toxicity of the Bt cotton varieties ZMSJ, which expresses the toxins Cry1Ac and Cry2Ab, and ZMKCKC, which expresses Cry1Ac and EPSPS, on adult A. suturalis and H. luteolus. No significant increase in the mortality of either species was detected after feeding on Bt cotton leaves or pollen for 7 days. Trace amounts of Cry1Ac and Cry2Ab proteins could be detected in both species but in vitro binding experiments found no evidence of Cry1Ac and Cry2Ab binding proteins. These results demonstrate that feeding on the leaves or pollen of these two Bt cotton varieties has no toxic effects on adult A. suturalis or H. luteolus.