Hydrocarbons, CO, NOx, NH₃, N₂O, CO₂ and particulate matter emissions affect air quality, global warming and human health. Transport sector is an important source of these pollutants and high pollution episodes are often experienced during the cold season. However, EU vehicle emissions regulation at cold ambient temperature only addresses hydrocarbons and CO vehicular emissions. For that reason, we have studied the impact that cold ambient temperatures have on Euro 6 diesel and spark ignition (including: gasoline, ethanol flex-fuel and hybrid vehicles) vehicle emissions using the World-harmonized Light-duty Test Cycle (WLTC) at −7 °C and 23 °C. Results indicate that when facing the WLTC at 23 °C the tested vehicles present emissions below the values set for type approval of Euro 6 vehicles (still using NEDC), with the exception of NOx emissions from diesel vehicles that were 2.3–6 times higher than Euro 6 standards. However, emissions disproportionally increased when vehicles were tested at cold ambient temperature (−7 °C). High solid particle number (SPN) emissions (>1 × 10¹¹ # km⁻¹) were measured from gasoline direct injection (GDI) vehicles and gasoline port fuel injection vehicles. However, only diesel and GDI SPN emissions are currently regulated. Results show the need for a new, technology independent, procedure that enables the authorities to assess pollutant emissions from vehicles at cold ambient temperatures.Harmful pollutant emissions from spark ignition and diesel vehicles are strongly and negatively affected by cold ambient temperatures. Only hydrocarbon, CO emissions are currently regulated at cold temperature. Therefore, it is of great importance to revise current EU winter vehicle emissions regulation.

China is now experiencing major public health challenges caused by air pollution. Few studies have quantified the dynamics of air pollution and its impact on the risk of respiratory infection. We conducted an integrated data analysis to quantify the association among air quality index (AQI), meteorological variables and respiratory infection risk in Shaanxi province of China in the period of November 15th, 2010 to November 14th, 2016. Our analysis illustrated a statistically significantly positive correlation between the number of influenza-like illness (ILI) cases and AQI, and the respiratory infection risk has increased progressively with increased AQI with a time lag of 0–3 days. We also developed mathematical models for the AQI trend and respiratory infection dynamics, incorporating AQI-dependent incidence and AQI-based behaviour change interventions. Our combined data and modelling analysis estimated the basic reproduction number for the respiratory infection during the studying period to be 2.4076, higher than the basic reproduction number of the 2009 pandemic influenza in the same province. Our modelling-based simulations concluded that, in terms of respiratory infection risk reduction, the persistent control of emission in the China's blue-sky programme is much more effective than substantial social-economic interventions implemented only during the smog days.

During the 20th century the impacts of industrialization and urbanization in Galveston Bay resulted in significant shifts in trace metals (Hg, Pb, Ni, Zn) and vascular plant biomarkers (lignin phenols) recorded within the surface sediments and sediment cores profile. A total of 22 sediment cores were collected in Galveston Bay in order to reconstruct the historical input of Hg, Pb, Ni, Zn and terrestrial organic matter. Total Hg (T-Hg) concentration ranged between 6 and 162 ng g−1 in surface sediments, and showed decreasing concentrations southward from the Houston Ship Channel (HSC) toward the open estuary. Core profiles of T-Hg and trace metals (Ni, Zn) showed substantial inputs starting in 1905, with peak concentrations between 1960 and 1970's, and decreasing thereafter with exception to Pb, which peaked around 1930–1940s. Stable carbon isotopes and lignin phenols showed an increasing input of terrestrial organic matter driven by urban development within the watershed in the early 1940s. Both the enrichment factor and the geoaccumulation index (Igeo) for T-Hg as a measure of the effectiveness of environmental management practices showed substantial improvements since the 1970s. The natural recovery rate in Galveston Bay since the peak input of T-Hg was non-linear and displayed a slow recovery during the twenty-first century.

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.

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.

The eutrophication of estuaries results from increasing anthropogenic nutrient inputs to coastal waters. Ecosystem recovery from eutrophication is partly dependent on the ability of a system to assimilate or remove nutrients, and denitrification and dissimilatory nitrate reduction to ammonium (DNRA) are important pathways for nitrogen (N) removal or retention. We measured rates of denitrification and DNRA over an annual cycle at two stations in Weeks Bay, AL, a shallow microtidal estuary receiving freshwater from two rivers with agricultural watersheds and high N inputs. We hypothesized that rates of DNRA would exceed denitrification in the sulfidogenic sediments in this estuary. Consistent with our hypothesis, we found that DNRA (44.4 ± 5.5 μmol N m−2 hr−1) exceeded in situ denitrification (0.9 ± 2.3 μmol N m−2 hr−1) and that even in the presence of abundant water column nitrate DNRA was favored over denitrification by a factor of two. DNRA is estimated to provide N to the water column at a rate equivalent to 15% of the N input that is retained within the estuary and is a significant component of the N budget in this highly impacted estuary. DNRA by retaining N in the system contributes to the N demand by primary producers and can impact this estuary through enhanced rates of primary production. Weeks Bay, like many coastal estuaries, experiences periods of hypoxia, blooms of harmful algae and fish kills. Future management efforts should focus on reducing nutrient input to this estuary without which the significant retention of N in this system through DRNA will contribute to the undesirable ecosystem attributes associated with eutrophication.

Arsenic, cadmium and lead are toxic environmental contaminants. They were shown to be associated with telomere length (TL) in adults. Although they can cross the placental barrier, the effect of prenatal exposure of these metals on newborn TL is unknown. The aim of this study was to examine whether prenatal exposure to heavy metals has an impact on newborn leucocyte TL. A birth-cohort study was conducted with 409 pregnant women and their newborns in Myanmar. During the first visit, face-to-face interviews were conducted, and maternal spot urine sampling was performed. Cord blood samples were collected during follow-up. Urinary heavy metal concentration was measured by ICP-MS and adjusted for creatinine. Relative TL was measured by quantitative real-time polymerase chain reaction. The extent of prenatal arsenic, cadmium and lead exposure and their associations with newborn leucocyte TL were assessed using multivariate linear regression. The median values of maternal urinary arsenic, cadmium, and lead concentrations were 73.9, 0.9, and 1.8 μg/g creatinine, respectively. Prenatal arsenic and cadmium exposure was significantly associated with newborn TL shortening (lowest vs highest quartile, coefficient = - 0.13, 95% CI: - 0.22, - 0.03, p = 0.002, and coefficient = - 0.17, 95% CI: - 0.27, - 0.07, p = 0.001, respectively), and the associations remained robust after adjusting for confounders. There was no significant association between prenatal lead exposure and newborn TL. The present study identified the effect of arsenic and cadmium exposure on TL shortening, even in utero exposure at a lower concentration.

Residues of pyrethroid insecticides tend to accumulate in bed sediments due to their strong hydrophobicity. Rather than the total or bulk sediment concentration, it is the freely dissolved concentration (Cfᵣₑₑ) that drives toxicity to benthic invertebrates. In this study we developed thin film-based samplers for in situ ambient monitoring of pyrethroids at trace levels in sediment. Out of five common polymer materials, polyethylene (PE) and silicone rubber (SR), were identified to offer superior enrichment for pyrethroids from sediment. To circumvent the slow equilibrium process, ¹³C-permethrin and bifenthrin-d₅ were preloaded onto the films as performance reference compounds (PRCs). The PRC-preloaded film samplers were deployed at five sites in Southern California under field conditions for 7 d and retrieved for analysis. The sediment porewater Cfᵣₑₑ of eight pyrethroids derived from PRC-PE films ranged from 173 to 903 ng/L, accounting for 18.2–36.1% of the corresponding total porewater concentrations. The PRC-SR film samplers yielded Cfᵣₑₑ values closely mimicking those from the PRC-PE samplers, cross-validating the two sampling devices. Additionally, a significant positive association was found between the observed mortality from toxicity tests using Hyalella azteca and the Cfᵣₑₑ of bifenthrin (r = 0.628, p = 0.02). A significant linear correlation (R² = 0.99) between Cfᵣₑₑ derived from in situ monitoring and that of ex situ measurement under equilibrium conditions was also observed. Results from this study demonstrated that the film-based samplers may be used for in situ ambient monitoring to detect biologically relevant contamination of pyrethroids in bed sediments, which may contribute to improved risk assessment for this class of widely used insecticides.

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.

Satellite-retrieved aerosol optical depth (AOD) is commonly used to estimate ambient levels of fine particulate matter (PM₂.₅), though it is important to mitigate the estimation bias of PM₂.₅ due to gaps in satellite-retrieved AOD. A nonparametric approach with two random-forest submodels is proposed to estimate PM₂.₅ levels by filling gaps in satellite-retrieved AOD. This novel approach was employed to estimate the spatiotemporal distribution of daily PM₂.₅ levels during 2013–2015 in the Sichuan Basin of Southwest China, where the coverage rate of composite AOD retrieved by the Terra and Aqua satellites was only 11.7%. Based on the retrieved AOD and various covariates (including meteorological conditions and land use types), the first random-forest submodel (named AOD-submodel) was trained to fill the gaps in the AOD dataset, giving a cross-validation R² of 0.95. Subsequently, the second random-forest submodel (named PM₂.₅-submodel) was trained to estimate the PM₂.₅ levels for unmonitored areas/days based on the gap-filled AOD, ground-monitored PM₂.₅ levels, and the covariates, and achieved a cross-validation R² of 0.86. By comparing the complete and incomplete (i.e., without the days when AOD data were missing) estimates, we found that the monthly PM₂.₅ levels could be overestimated by 34.6% if the PM₂.₅ values coincident with AOD gaps were not considered. The newly developed approach is valuable for deriving the complete spatiotemporal distribution of daily PM₂.₅ from incomplete remote-sensing data, which is essential for air quality management and human exposure assessment.