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.

Moss (as a reference material) and camphor (Cinnamomum Camphora) leaf, branch bark and bark samples were systematically collected across an urban-rural gradient in Guiyang (SW China) to determine the efficacy of using these bio-indicators to evaluate nitrogen (N) pollution. The tissue N concentrations (0.13%–2.70%) and δ¹⁵N values (−7.5‰ to +9.3‰) of all of these bio-indicators exhibited large spatial variations, as they recorded higher values in urban areas that quickly decreased with distance from the city center; moreover, both soil N concentrations and soil δ¹⁵N values were found no significant differences within each 6 km from the urban to the rural area. This not only suggests that the different N uptake strategies and variety of N responses of these bio-indicators can be reflected by their different susceptibilities to variations in N deposition but also reveals that they are able to indicate that urban N deposition is mostly from traffic and industry (NOₓ-N), whereas rural N deposition is mainly from agriculture (NHₓ-N). Compared to previously collected urban moss and camphor leaf samples, the significantly increased δ¹⁵N values in current urban moss and camphor leaf samples further indicate a greater contribution of NOₓ-N than NHₓ-N to urban N deposition. The feasibility of using the N concentrations and δ¹⁵N values of branch bark and bark as biomarkers of N deposition thus was further confirmed through the comparative use of these bio-indicators. It can be concluded that vascular plant leaves, branch bark and bark can be used as useful biomonitoring tools for evaluating atmospheric N pollution. For further study, quantitative criteria for the practical use of these bio-indicators in response to N deposition should be developed and the differences in the δ¹⁵N values of different plant parts should also be considered, particularly in urban environments that are severely disrupted by atmospheric pollution.

Natural gas extraction (NGE) has expanded rapidly in the United States in recent years. Despite concerns, there is little information about the effects of NGE on air quality or personal exposures of people living or working nearby. Recent research suggests NGE emits polycyclic aromatic hydrocarbons (PAHs) into air. This study used low-density polyethylene passive samplers to measure concentrations of PAHs in air near active (n = 3) and proposed (n = 2) NGE sites. At each site, two concentric rings of air samplers were placed around the active or proposed well pad location. Silicone wristbands were used to assess personal PAH exposures of participants (n = 19) living or working near the sampling sites. All samples were analyzed for 62 PAHs using GC-MS/MS, and point sources were estimated using the fluoranthene/pyrene isomer ratio. ∑PAH was significantly higher in air at active NGE sites (Wilcoxon rank sum test, p < 0.01). PAHs in air were also more petrogenic (petroleum-derived) at active NGE sites. This suggests that PAH mixtures at active NGE sites may have been affected by direct emissions from petroleum sources at these sites. ∑PAH was also significantly higher in wristbands from participants who had active NGE wells on their properties than from participants who did not (Wilcoxon rank sum test, p < 0.005). There was a significant positive correlation between ∑PAH in participants' wristbands and ∑PAH in air measured closest to participants’ homes or workplaces (simple linear regression, p < 0.0001). These findings suggest that living or working near an active NGE well may increase personal PAH exposure. This work also supports the utility of the silicone wristband to assess personal PAH exposure.

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.

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.

Low-cost air quality sensors offer high-resolution spatiotemporal measurements that can be used for air resources management and exposure estimation. Yet, such sensors require frequent calibration to provide reliable data, since even after a laboratory calibration they might not report correct values when they are deployed in the field, due to interference with other pollutants, as a result of sensitivity to environmental conditions and due to sensor aging and drift. Field calibration has been suggested as a means for overcoming these limitations, with the common strategy involving periodical collocations of the sensors at an air quality monitoring station. However, the cost and complexity involved in relocating numerous sensor nodes back and forth, and the loss of data during the repeated calibration periods make this strategy inefficient. This work examines an alternative approach, a node-to-node (N2N) calibration, where only one sensor in each chain is directly calibrated against the reference measurements and the rest of the sensors are calibrated sequentially one against the other while they are deployed and collocated in pairs. The calibration can be performed multiple times as a routine procedure. This procedure minimizes the total number of sensor relocations, and enables calibration while simultaneously collecting data at the deployment sites. We studied N2N chain calibration and the propagation of the calibration error analytically, computationally and experimentally. The in-situ N2N calibration is shown to be generic and applicable for different pollutants, sensing technologies, sensor platforms, chain lengths, and sensor order within the chain. In particular, we show that chain calibration of three nodes, each calibrated for a week, propagate calibration errors that are similar to those found in direct field calibration. Hence, N2N calibration is shown to be suitable for calibration of distributed sensor networks.

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.