Hexavalent chromium (Cr(VI)) reduction by Geobacter sulfurreducens PCA was evaluated in batch experiments, and the form and amounts of intracellular and extra-cellular Cr(VI) reduction products were determined over time. The first-order Cr(VI) reduction rate per unit mass of cells was consistent for different initial cell concentrations, and approximately equal to (2.065 ± 0.389) x 10−9 mL CFU−1 h−1. A portion of the reduced Cr(VI) products precipitated on Geobacter cell walls as Cr(III) and was bound via carboxylate functional groups, a portion accumulated inside Geobacter cells, and another portion existed as soluble Cr(III) or organo-Cr(III) released to solution. A mass balance analysis of total chromium in aqueous media, on cell walls, and inside cells was determined as a function of time, and with different initial cell concentrations. Mass balances were between 92% and 98%, and indicated Cr(VI) reduction products accumulate more on cell walls and inside cells with time and with increasing initial cell concentration, as opposed to particulates in aqueous solution. Reduced Cr(VI) products both in solution and on cell surfaces appear to form organo-Cr(III) complexes, and our results suggest that such complexes are more stable to reoxidation than aqueous Cr(III) or Cr(OH)3. Chromium inside cells is also likely more stable to reoxidation, both because it can form organic complexes, and it is separated by the cell membrane from solution conditions. Hence, Cr(VI) reduction products in groundwater during bioremediation may become more stable against re-oxidation, and may pose a lower risk to human health, over time and with greater initial biomass densities.

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM₂.₅) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM₂.₅ monitoring data was supplemented by imputing PM₂.₅ concentrations at PM₁₀ sites, using PM₂.₅/PM₁₀ ratios at co-located sites. Daily PM₂.₅ concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM₂.₅ in cells with AOD but without PM₂.₅ measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM₂.₅ predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM₂.₅ concentrations.

PM₂.₅ pollution poses severe health risks to urban residents in low and middle-income countries. Existing studies have shown that the problem is affected by multiple socioeconomic factors. However, the relative contribution of these factors is not well understood, which sometimes leads to controversial controlling measures. In this study, we quantified the relative contribution of different socioeconomic factors, including the city size, industrial activities, and residents' activities, to PM₂.₅ pollution in urban China between 2014 and 2015 by using structural equation model (SEM). Our results showed that industrial activities contributed more to PM₂.₅ pollution than other factors. The city size and residents’ activities also had significant impacts on PM₂.₅ pollution. The combined influence of all socioeconomic factors could explain between 44% and 48% of variation in PM₂.₅ pollution, which indicated the existence of influences from other factors such as weather conditions and outside sources of pollutants. Findings from our study can contribute to a more comprehensive understanding of the socioeconomic causes of PM₂.₅ pollution.

Air pollutant levels depend on emissions but can also be affected by the meteorological situation. We examined air pollutant trends (PM₁₀, NO₂, O₃ and SO₂) in Slovenia, where in the past the main issue were SO₂ levels. Now, the population is still exposed to PM₁₀ and ozone levels that are above the recommended levels.Our goal was to assess if the levels of air pollutants were decreasing from 2002 to 2017 due to emission ceilings or were more influenced by changes in the meteorological situation. We modelled the relationship between levels, meteorological parameters, and seasonality and then used the models with the best estimated generalisation to adjust levels for meteorology. Models showed a significant relationship between meteorological parameters and PM₁₀, NO₂, and O₃ levels, but not SO₂. We analysed trends of raw and adjusted levels and compared them. Trends of PM₁₀ and SO₂ were decreasing at all locations for raw and adjusted data. The largest decrease was observed in SO₂ levels where the largest decrease in emissions occurred. Trends of NO₂ were also significant and negative at most locations. Levels of O₃ did not exhibit a significant trend at most locations.Results show that changes in the meteorological situation affected PM₁₀ levels the most, especially where the entire period (2002–2017) could be observed. There is strong empirical evidence that changes in meteorological parameters contributed to the decrease in PM₁₀ levels while the decrease in NO₂ and SO₂ levels can be attributed to emission ceilings.

Ambient concentrations of fine particulate matter (PM₂.₅) concentration is often used as an exposure surrogate to estimate PM₂.₅ health effects in epidemiological studies. Ignoring the potential variations in the amount of outdoor PM₂.₅ infiltrating into indoor environments will cause exposure misclassification, especially when people spend most of their time indoors. As it is not feasible to measure the PM₂.₅ infiltration factor (Fᵢₙf) for each individual residence, we aimed to build models for residential PM₂.₅Fᵢₙf prediction and to evaluate seasonal Fᵢₙf variations among residences. We repeated collected paired indoor and outdoor PM₂.₅ filter samples for 7 continuous days in each of the three seasons (hot, cold and transitional seasons) from 48 typical homes of Shanghai, China. PM₂.₅-bound sulfur on the filters was measured by X-ray fluorescence for PM₂.₅Fᵢₙf calculation. We then used stepwise-multiple linear regression to construct season-specific models with climatic variables and questionnaire-based predictors. All models were evaluated by the coefficient of determination (R²) and root mean square error (RMSE) from a leave-one-out-cross-validation (LOOCV). The 7-day mean (±SD) of PM₂.₅Fᵢₙf across all observations was 0.83 (±0.18). Fᵢₙf was found higher and more varied in transitional season (12–25 °C) than hot (>25 °C) and cold (<12 °C) seasons. Air conditioning use and meteorological factors were the most important predictors during hot and cold seasons; Floor of residence and building age were the best transitional season predictors. The models predicted 60.0%–68.4% of the variance in 7-day averages of Fᵢₙf, The LOOCV analysis showed an R² of 0.52 and an RMSE of 0.11. Our finding of large variation in residential PM₂.₅Fᵢₙf between seasons and across residences within season indicated the important source of outdoor-generated PM₂.₅ exposure heterogeneity in epidemiologic studies. Our models based on readily available data may potentially improve the accuracy of estimates of the health effects of PM₂.₅ exposure.

It is critical to estimate the exposure to indoor air pollution of residents spending most of their time in such microenvironments. However, the understanding regarding PM2.5 exposure in residential indoor environments is very limited. In this study, we collected participants' basic information and time–activity patterns, as well as details of other factors related to indoor air pollution exposure, through questionnaires presented to a large population in 37 counties of China. Continuous monitoring of ambient PM2.5 concentrations was performed using an environmental fixed-site monitoring network. Residential indoor PM2.5 concentrations were predicted using a mass balance model based on the data obtained. Evaluation of continuous daily average residential indoor PM2.5 exposure doses for large populations during winter revealed concentrations ranged from 67 to 195 μg/m3. Finally, differences in residential indoor PM2.5 exposure between northern and southern China were investigated. The results suggested that residential indoor PM2.5 concentrations in northern China, associated with heating, were higher than in the south. The established model could be important for improved understanding of human exposure to indoor PM2.5 air pollution.

Solid fuels, an important source of severe Household Air Pollution (HAP) linked to many adverse health outcomes, has been widely consumed around the world. China consumes large amounts of solid fuels and suffers from serious indoor and outdoor air pollution. Though global HAP issues had been reviewed in previous literatures, peer-reviewed Chinese publications were seldom included in those reviews. We conducted a literature review on the studies of HAP and personal exposure in rural China with inputs from peer-reviewed publications in both English and Chinese. A total of 36,572 articles were retrieved, 294 were read in full text, of which 92 were included in final data extraction and in-depth analysis. Although HAP is a very serious issue in China, studies on either HAP or personal exposure assessment were very limited. From existing studies, levels of air pollutants including carbon monoxide, sulfur dioxide, particulate matter (PM), organic carbon, elemental carbon, polycyclic aromatic hydrocarbons (PAHs), etc., in indoor and ambient air were analyzed for their temporal and spatial variations, and the differences across different fuel types were compared. The studies showed that PM and PAHs levels in most rural homes exceeded the World Health Organization (WHO) and Chinese National Standards, especially during the heating season in northern China. Replacing traditional fuels with cleaner ones (such as liquid petroleum gas (LPG), biogas or electricity) was considered as the most appropriate way to mitigate HAP. The daily exposure to PM and PAHs from using LPG, biogas or electricity was considerably lower than that from using traditional solid fuels. However, the level was still higher than the guideline values for PM and PAHs set by WHO to protect human health. To achieve a more effective control, the current data gap need to be closed and suggestions for future research were discussed in this review.

Poultry-emitted air pollutants, including particulate matter (PM) and ammonia, have raised concerns due to potential negative effects on human health and the environment. However, developing and optimizing remediation technologies requires a better understanding of air pollutant concentrations, the emission plumes, and the relationships between the pollutants. Therefore, we conducted ten field experiments to characterize PM (total suspended particulate [TSP], particulate matter less than 10 μm in aerodynamic diameter [PM10], and particulate matter less than 2.5 μm in aerodynamic diameter [PM2.5]) and ammonia emission-concentration profiles from a typical commercial poultry house. The emission factors of the poultry house, which were calculated using the concentrations and fan speed, were 0.66 (0.29–0.99) g NH3-N bird−1d−1 for ammonia, 52 (44–168) g d−1AU−1 (AU = animal unit = 500 kg) for TSP, 3.48 (1.16–9.03) g d−1AU−1 for PM10, and 0.07 (0.00–0.36) g d−1AU−1 for PM2.5. PM and ammonia emission concentrations decreased as distance from the fan increased. Although emission concentrations were similar in the daytime and nighttime, diurnal and nocturnal plume shapes were different due to the increased stability of the atmosphere at night. Particle size distribution analysis revealed that, at a given height, the percentage of PM10 and PM2.5 was consistent throughout the plume, indicating that the larger particles were not settling out of the airstream faster than the smaller particles. Overall, the direction of the measured air pollutant emission plumes was dominated by the tunnel fan ventilation airflow rate and direction instead of the ambient wind speed and direction. This is important because currently-available air dispersion models use ambient or modeled wind speed and direction as input parameters. Thus, results will be useful in evaluating dispersion models for ground-level, horizontally-released, point sources and in developing effective pollutant remediation strategies for emissions.

Although several air pollution studies have examined the relationship between people living close to roadways and human health, we are unaware of studies that have examined the distance-decay of this effect based on a snapshot of congestion and focused on a micro-level traffic emission inventory. In this paper we estimate the distance-decay of premature mortality risk related to PM₂.₅ emitted by traffic congestion in Hamilton, Canada, in 2011 We employ the Stochastic User Equilibrium (SUE) traffic assignment algorithm to estimate congested travel times for each road link in our study area. Next, we used EPA's MOVES model to estimate mass of PM₂.₅, and then R-line dispersion model to predict concentration of PM₂.₅. Finally, we apply Integrated Exposure Response Function (IERF) to estimate PM₂.₅-related premature mortality at 100 m × 100 m grid resolution. We estimated total premature mortality over Hamilton to be 73.10 (95%CI: 39.05; 82.11) deaths per year. We observed that the proximity to a roadway increases the risk of premature mortality and the strength of this risk decreases as buffer sizes are increased. For example, we estimated that the premature mortality risk within buffer 0–100 m is 29.5% higher than for the buffer 101–200 m, 179.3% higher than for the buffer 201–300 m, and 566% higher than for the buffer 301–400 m. Our study provides a new perspective on exposure increments from traffic congestion. In particular, our findings show health effects gradients across neighborhoods, capturing microscale near-road exposure up to 2000 m of the roadway. Results from this research can be useful for policymakers to develop new strategies for the challenges of regulating transportation, land use, and air pollution.

Exposure to ambient air pollution has been linked with adverse health outcomes of the circulatory and nervous systems. Given that falls are closely related to circulatory and nervous health, we hypothesize that air pollution may adversely affect fall-related injury. We employed Wave 1 data from 36,662 participants aged ≥50 years in WHO's Study on Global AGEing and Adult Health in six low- and middle-income countries. Ambient annual concentration of PM2.5 was estimated using satellite data. A three-level logistic regression model was applied to examine the long-term association between ambient PM2.5 and the prevalence of fall-related injury, and associated disease burden, as well as the potential effect modification of consumption of fruit and vegetables. Ambient PM2.5 was found to be significantly associated with the risk of fall-related injury. Each 10 μg/m3 increase corresponded to 18% (OR = 1.18, 95% CI: 1.09, 1.28) increase in fall-related injury after adjusting for various covariates. The association was relatively stronger among participants with lower consumption of fruit (OR = 1.22, 95% CI: 1.12, 1.33) than higher consumption (OR = 1.06, 95% CI: 0.92, 1.23), and among those with lower vegetable consumption (OR = 1.18, 95% CI: 1.08, 1.28) than higher consumption (OR = 1.08, 95% CI: 0.91, 1.27). Our study suggests that ambient PM2.5 may be one risk factor for fall-related injury and that higher consumption of fruit and vegetables could alleviate this effect.