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.

Soil aggregates are often considered the basic structural elements of soils. Aggregates of different size vary in their ability to retain or transfer heavy metals in the environment. Here, after incubation of a sieved (<2 mm) topsoil with copper, bulk soil was separated into four aggregate-size fractions and their adsorption characteristics for Cu were determined. By combining nano-scale secondary ion mass spectrometry and C-1s Near Edge X-ray Absorption Fine Structure Spectroscopy, we found that copper tends to bind onto organic matter in the <2 μm and 20–63 μm aggregates. Surprisingly, Cu correlated with carboxyl-C in the <2 μm aggregates but with alkyl-C in the 20–63 μm aggregates. This is the first attempt to visualize the spatial distribution of copper in aggregate size fractions. These direct observations can help improve the understanding of interactions between heavy metals and various soil components.

Herbicides are increasingly recognised as sources of water pollution. Glyphosate-based herbicides (GBHs) are widely used because of their low cost and high effectiveness. By measuring the photosynthetic efficiency of Fucus virsoides fronds exposed to a GBH (Roundup® Power 2.0), we investigated the effect of a continuous exposure (6 days) and the potential of recovery after a short exposure (24 h). Both experiments were carried out combining GBH with and without nutrient enrichment, simulating a runoff event. A factorial experimental design allowed us to assess the potential of interactions between GBH and nutrients, which are likely to co-occur in coastal areas. Our results show deleterious effects of GBH at low concentration on F. virsoides, independently from the duration of exposure and the presence of nutrients.

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.

In the present study, the daily dose in terms of particle surface area received by citizens living in five cities in Western countries, characterized by different lifestyle, culture, climate and built-up environment, was evaluated and compared. For this purpose, the exposure to sub-micron particle concentration levels of the population living in Barcelona (Spain), Cassino (Italy), Guilford (United Kingdom), Lund (Sweden), and Brisbane (Australia) was measured through a direct exposure assessment approach. In particular, measurements of the exposure at a personal scale were performed by volunteers (15 per each population) that used a personal particle counter for different days in order to obtain exposure data in microenvironments/activities they resided/performed. Non-smoking volunteers performing non-industrial jobs were considered in the study.Particle concentration data allowed obtaining the exposure of the population living in each city. Such data were combined in a Monte Carlo method with the time activity pattern data characteristics of each population and inhalation rate to obtain the most probable daily dose in term of particle surface area as a function of the population gender, age, and nationality.The highest daily dose was estimated for citizens living in Cassino and Guilford (>1000 mm²), whereas the lowest value was recognized for Lund citizens (around 100 mm²). Indoor air quality, and in particular cooking and eating activities, was recognized as the main influencing factor in terms of exposure (and thus dose) of the population: then confirming that lifestyle (e.g. time spent in cooking activities) strongly affect the daily dose of the population. On the contrary, a minor or negligible contribution of the outdoor microenvironments was documented.

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.

Studying the characteristics of new particle formation (NPF) is important as it is generally recognized as a major contributor to particle pollution in urban environments. We investigated NPF events that occurred during a 1-year period in the urban environment of Brisbane, Australia, using a neutral cluster and air ion spectrometer (NAIS) which is able to monitor both neutral and charged particles and clusters down to a size of 0.8 nm. NPF events occurred on 41% of days, with the occurrence rate of 7% greater in the summer than in the winter. We derived the first diurnal event distribution of NPF events anywhere in the world and showed that the most probable starting time of an NPF event was near 08:30 a.m., being about an hour earlier in the winter than in the summer. During NPF days, 10% of particles were charged. The mean neutral and charged particle concentrations on NPF days were, respectively, 49% and 14% higher than those on non-event days. The mean formation rate of 2–3 nm particles during an NPF event was 20.8 cm⁻³ s⁻¹. The formation rate of negatively charged particles was about 10% higher than that of positively charged particles. The mean particle growth rate in the size range up to 20 nm was 6.2 nm h⁻¹. These results are compared and contrasted with corresponding values that have been derived with the scanning mobility particle sizer (SMPS) at the same location and with values that have been reported with the NAIS at other locations around the world. This is the first comprehensive study of the characteristics of NPF events over a significantly long period in Australia.

PM₁ might be more hazardous than PM₂.₅ (particulate matter with an aerodynamic diameter ≤ 1 μm and ≤2.5 μm, respectively). However, studies on PM₁ concentrations and its health effects are limited due to a lack of PM₁ monitoring data.To estimate spatial and temporal variations of PM₁ concentrations in China during 2005–2014 using satellite remote sensing, meteorology, and land use information.Two types of Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) data, Dark Target (DT) and Deep Blue (DB), were combined. Generalised additive model (GAM) was developed to link ground-monitored PM₁ data with AOD data and other spatial and temporal predictors (e.g., urban cover, forest cover and calendar month). A 10-fold cross-validation was performed to assess the predictive ability.The results of 10-fold cross-validation showed R² and Root Mean Squared Error (RMSE) for monthly prediction were 71% and 13.0 μg/m³, respectively. For seasonal prediction, the R² and RMSE were 77% and 11.4 μg/m³, respectively. The predicted annual mean concentration of PM₁ across China was 26.9 μg/m³. The PM₁ level was highest in winter while lowest in summer. Generally, the PM₁ levels in entire China did not substantially change during the past decade. Regarding local heavy polluted regions, PM₁ levels increased substantially in the South-Western Hebei and Beijing-Tianjin region.GAM with satellite-retrieved AOD, meteorology, and land use information has high predictive ability to estimate ground-level PM₁. Ambient PM₁ reached high levels in China during the past decade. The estimated results can be applied to evaluate the health effects of PM₁.

A massive and dense textured layer (ca. 35–50 cm thick) of hardpan was uncovered at the top layer, which capped the unweathered sulfidic Cu-Pb-Zn tailings in depth and physically supported gravelly soil root zones sustaining native vegetation for more than a decade. For the purpose of understanding functional roles of the hardpan layer in the cover profile, the present study has characterized the microstructures of the hardpan profile at different depth compared with the tailings underneath the hardpans. A suit of microspectroscopic technologies was deployed to examine the hardpan samples, including field emission-scanning electron microscopy coupled with energy dispersive spectroscopy (FE-SEM-EDS), X-ray diffraction (XRD) and synchrotron-based X-ray absorption fine structure spectroscopy (XAFS). The XRD and Fe K-edge XAFS analysis revealed that pyrite in the tailings had been largely oxidised, while goethite and ferrihydrite had extensively accumulated in the hardpan. The percentage of Fe-phyllosilicates (e.g., biotite and illite) decreased within the hardpan profile compared to the unweathered tailings beneath the hardpan. The FE-SEM-EDS analysis showed that the fine-grained Ca-sulfate (possibly gypsum) evaporites appeared as platelet-shaped that deposited around pyrite, dolomite, and crystalline gypsum particles, while Fe-Si gels exhibited a needle-like texture that aggregated minerals together and produced contiguous coating on pyrite surfaces. These microstructural findings suggest that the weathering of pyrite and Fe-phyllosilicates coupled with dolomite dissolution may have contributed to the formation of Ca-sulfate/gypsum evaporites and Fe-Si gels. These findings have among the first to uncover the microstructure of hardpan formed at the top layer of sulfidic Cu-Pb-Zn tailings, which physically capped the unweathered tailings in depth and supported root zones and native vegetation under semi-arid climatic conditions.

Permeable reactive barriers (PRBs) remove nitrogen from groundwater by enhancing microbial denitrification. The PRBs consist of woodchips that provide carbon for denitrifiers, but these woodchips also support other anaerobic microbes, including sulfate-reducing bacteria. Some of these anaerobes have the ability to methylate inorganic mercury present in groundwater. Methylmercury is hazardous to human health, so it is essential to understand whether PRBs promote mercury methylation. We examined microbial communities and geochemistry in fresh water and sulfate-enriched PRB flow-through columns by spiking replicates of both treatments with mercuric chloride. We hypothesized that mercury addition could alter bacterial community composition to favor higher abundances of genera containing known methylating taxa and that the sulfate-rich columns would produce more methylmercury after mercury addition, due mainly to an increase in abundance of sulfate reducing bacteria (SRB). However, methylmercury output at the end of the experiment was not different from output at the beginning, due in part to coupled Hg methylation and demethylation. There was a transient reduction in nitrate removal after mercury addition in the sulfate enriched columns, but nitrate removal returned to initial rates after two weeks, demonstrating resilience of the denitrifying community. Since methylmercury output did not increase and nitrate removal was not permanently affected, PRBs could be a low cost approach to combat eutrophication.