Construction workers on highway rehabilitation projects can be exposed to a combination of traffic- and construction-related emissions. To assess the personal exposure a worker experiences, a portable battery-operated Air Quality Device (AQD) was utilised to measure emissions during normal construction operations of a major road rehabilitation project. Emissions measured were nitrogen dioxide (NO₂), Total Volatile Organic Compounds (TVOCs) and Particulate Matter (PM₁₀, PM₂.₅, and PM₁). The objective of the paper is to document the hazardous emissions that construction workers may be exposed to and allow for a basis of informed decision making to mitigate the risks of a road construction project. Most critically, this article is designed to raise awareness of the potential impact to a worker's wellbeing as well as highlight the need for further research. Through statistical analysis, asphalt paving was identified as the most hazardous activity in terms of exposure relative to other activities. This activity was further assessed using discrete-time Markov chain Monte Carlo simulations with results indicating a high probability that workers may be exposed to greater hazardous emission concentrations than measured. Limiting the distance to the source of emissions, large-scale use of warm-mix asphalt and reducing the idling times of construction vehicles were identified as practical mitigation measures to reduce exposure and aid in achieving zero-harm objectives. Finally, it is found that males are more susceptible to long-term implications of hazardous emission inhalation and should be more aware if the scenarios they might work in expose them to this.

Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO₂) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO₂ vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 10¹⁶ molecules cm⁻² in the winter due to enhanced NO₂ emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO₂ VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO₂ VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 10¹⁵ molecules cm⁻² in the summer, autumn and winter, respectively, due to increased NO₂ emissions, and then decreased by 2.8, 4.2, and 5.1 × 10¹⁵ molecules cm⁻² at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO₂ VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO₂ VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO₂/SO₂ ratio was used to identify NO₂ from industrial sources and vehicle exhaust. The contribution of industrial NO₂ VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO₂. Our results suggest that air quality management in Shenyang should consider reductions in local NO₂ emissions from industrial sources along with regional cooperative control.

Individual-level studies with adjustment for important COVID-19 risk factors suggest positive associations of long-term air pollution exposure (particulate matter and nitrogen dioxide) with COVID-19 infection, hospitalisations and mortality. The evidence, however, remains limited and mechanisms unclear. We aimed to investigate these associations within UK Biobank, and to examine the role of underlying chronic disease as a potential mechanism. UK Biobank COVID-19 positive laboratory test results were ascertained via Public Health England and general practitioner record linkage, COVID-19 hospitalisations via Hospital Episode Statistics, and COVID-19 mortality via Office for National Statistics mortality records from March–December 2020. We used annual average outdoor air pollution modelled at 2010 residential addresses of UK Biobank participants who resided in England (n = 424,721). We obtained important COVID-19 risk factors from baseline UK Biobank questionnaire responses (2006–2010) and general practitioner record linkage. We used logistic regression models to assess associations of air pollution with COVID-19 outcomes, adjusted for relevant confounders, and conducted sensitivity analyses. We found positive associations of fine particulate matter (PM₂.₅) and nitrogen dioxide (NO₂) with COVID-19 positive test result after adjustment for confounders and COVID-19 risk factors, with odds ratios of 1.05 (95% confidence intervals (CI) = 1.02, 1.08), and 1.05 (95% CI = 1.01, 1.08), respectively. PM 2.5 and NO 2 were positively associated with COVID-19 hospitalisations and deaths in minimally adjusted models, but not in fully adjusted models. No associations for PM₁₀ were found. In analyses with additional adjustment for pre-existing chronic disease, effect estimates were not substantially attenuated, indicating that underlying chronic disease may not fully explain associations. We found some evidence that long-term exposure to PM₂.₅ and NO₂ was associated with a COVID-19 positive test result in UK Biobank, though not with COVID-19 hospitalisations or deaths.

Air pollution has become a major issue in China, especially for traffic-related pollutants such as nitrogen dioxide (NO₂). Current studies in China at the national scale were less focused on NO₂ exposure and consequent health effects than fine particulate exposure, mainly due to a lack of high-quality exposure models for accurate NO₂ predictions over a long period. We developed an advanced modeling framework that incorporated multisource, high-quality predictor data (e.g., satellite observations [Ozone Monitoring Instrument NO₂, TROPOspheric Monitoring Instrument NO₂, and Multi-Angle Implementation of Atmospheric Correction aerosol optical depth], chemical transport model simulations, high-resolution geographical variables) and three independent machine learning algorithms into an ensemble model. The model contains three stages: (1) filling missing satellite data; (2) building an ensemble model and predicting daily NO₂ concentrations from 2013 to 2019 across China at 1×1 km² resolution; (3) downscaling the predictions to finer resolution (100 m) at the urban scale. Our model achieves a high performance in terms of cross-validation to assess the agreement of the overall (R² = 0.72) and the spatial (R² = 0.85) variations of the NO₂ predictions over the observations. The model performance remains moderately good when the predictions are extrapolated to the previous years without any monitoring data (CV R² > 0.68) or regions far away from monitors (CV R² > 0.63). We identified a clear decreasing trend of NO₂ exposure from 2013 to 2019 across the country with the largest reduction in suburban and rural areas. Our downscaled model further improved the prediction ability by 4%–14% in some megacities and captured substantial NO₂ variations within 1-km grids in the urban areas, especially near major roads. Our model provides flexibility at both temporal and spatial scales and can be applied to exposure assessment and epidemiological studies with various study domains (e.g., national or citywide) and settings (e.g., long-term and short-term).

DO₃SE (Deposition of Ozone for Stomatal Exchange), is a dry deposition model, designed to assess tropospheric ozone risk to vegetation, and is based on two alternative algorithms to estimate stomatal conductance: multiplicative and photosynthetic. The multiplicative model has been argued to perform better for leaf-level and regional-level application. In this study, we demonstrate that the photosynthetic model is superior to the multiplicative model even for leaf-level studies using measurements performed on Mangifera indica. We find that the multiplicative model overestimates the daytime stomatal conductance, when compared with measured stomatal conductance and prescribes zero conductance at night while measurements show an average conductance of 100 mmol(H₂O)m⁻²s⁻¹ between 9 p.m. and 4 a.m. The daytime overestimation of the multiplicative model can be significantly reduced when the model is modified to include a response function for ozone-induced stomatal closure. However, nighttime pollutant uptake fluxes can only be accurately assessed with the photosynthetic model which includes the stomatal opening at night during respiration and is capable of reproducing the measured nighttime stomatal conductance. At our site, the nocturnal flux contributes 64%, 39%, 46%, and 88% of the total for NO₂ uptake in winter, summer, monsoon, and post-monsoon, respectively. For SO₂, nocturnal uptake amounts to 35%, 28%, 28%, and 44% in winter, summer, monsoon, and post-monsoon, respectively while for ozone the nighttime uptake contributes 30%, 17%, 18%, and 29% of the total stomatal uptake in winter, summer, monsoon, and post-monsoon respectively.

Growing epidemiological evidence has shown that exposure to ambient air pollution contributes to poor sleep quality. However, whether variability in air pollution exposure affects sleep quality remains unclear. Based on a large sample in China, this study linked individual air pollutant exposure levels and temporal variability with subjective sleep quality. Town-level data on daily air pollution concentration for 30 days prior to the survey date were collected, and the monthly mean value, standard deviations, number of heavily polluted days, and trajectory for six common pollutants were calculated to measure air pollution exposure and its variations. Sleep quality was subjectively assessed using the Pittsburgh Sleep Quality Index (PSQI), and a PSQI score above 5 indicated overall poor sleep quality. Multilevel and negative control models were used. Both air pollution exposure and variability contributed to poor sleep quality. A one-point increase in the one-month mean concentration of particulate matter with aerodynamic diameters of ≤2.5 μm (PM₂.₅) and ≤10 μm (PM₁₀) led to 0.4% (95% confidence interval (CI): 1.002–1.006) and 0.3% (95% CI: 1.001–1.004) increases in the likelihoods of overall poor sleep quality (PSQI score >5), respectively; the odds ratios of a heavy pollution day with PM₂.₅ and PM₁₀ were 2.2% (95% CI: 1.012–1.032) and 2.2% (95% CI: 1.012–1.032), respectively. Although the mean concentrations of nitrogen dioxide, sulfur dioxide, and carbon monoxide met the national standard, they contributed to the likelihood of overall poor sleep quality (PSQI score >5). A trajectory of air pollution exposure with maximum variability was associated with a higher likelihood of overall poor sleep quality (PSQI score >5). Subjective measures of sleep latency, duration, and efficiency (derived from PSQI) were affected in most cases. Thus, sleep health improvements should account for air pollution exposure and its variations in China under relatively high air pollution levels.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors’ readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran’s I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Nitrogen dioxide (NO₂) is an important air pollutant and highly related to air quality, short- and long-term health effects, and even climate. A national model was developed using the extreme gradient boosting algorithm with high-resolution tropospheric vertical column NO₂ densities from the Sentinel-5 Precursor/Tropospheric Monitoring Instrument and general meteorological variables as input to generate daily mean surface NO₂ concentrations across mainland China. Model-derived daily NO₂ estimates were high accuracy with sample-based cross-validation coefficient of determination of 0.83, a root-mean-square error of 7.58 μg/m³, a mean prediction error of 5.56 μg/m³, and a mean relative prediction error of 18.08%. It has good performance in NO₂ estimations at both regional and individual site scale. The model also performed well in terms of estimating monthly, seasonal, and annual mean NO₂ concentrations across China. The model performance appears to better than or comparable to most previous related studies. The seasonal and annual spatial distributions of surface NO₂ across China and several regional NO₂ hotspots in 2019 were derived from the model and analyzed. Also evaluated were the population exposure levels of NO₂ for cities in and provinces of China. At the national scale, about 12% of the population experienced annual mean NO₂ concentrations exceeding the Chinese national air quality standard. The nationwide model with conventional predictors developed here can derive high-resolution surface NO₂ concentrations across China routinely, benefitting air epidemiological and environmental related studies.

Nitrogen dioxide (NO₂) is an important air pollutant that causes direct harms to the environment and human health. Ground NO₂ mapping with high spatiotemporal resolution is critical for fine-scale air pollution and environmental health research. We thus developed a spatiotemporal regression kriging model to map daily high-resolution (3-km) ground NO₂ concentrations in China using the Tropospheric Monitoring Instrument (TROPOMI) satellite retrievals and geographical covariates. This model combined geographically and temporally weighted regression with spatiotemporal kriging and achieved robust prediction performance with sample-based and site-based cross-validation R² values of 0.84 and 0.79. The annual mean and standard deviation of ground NO₂ concentrations from June 1, 2018 to May 31, 2019 were predicted to be 15.05 ± 7.82 μg/m³, with that in 0.6% of China’s area (10% of the population) exceeding the annual air quality standard (40 μg/m³). The ground NO₂ concentrations during the coronavirus disease (COVID-19) period (January and February in 2020) was 14% lower than that during the same period in 2019 and the mean population exposure to ground NO₂ was reduced by 25%. This study was the first to use TROPOMI retrievals to map fine-scale daily ground NO₂ concentrations across all of China. This was also an early application to use the satellite-estimated ground NO₂ data to quantify the impact of the COVID-19 pandemic on the air pollution and population exposures. These newly satellite-derived ground NO₂ data with high spatiotemporal resolution have value in advancing environmental and health research in China.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia’s most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series’ of nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide CO and particulate matter with diameter < 2.5 μm (PM₂.₅), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM₂.₅ (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM₂.₅ 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6–343.) excess deaths and 3490.0 (95% CI 1325.9–5653.5) additional hospitalisations were likely as a result of PM₂.₅ and O₃ exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO₂ decreased by 21.5 and 8% in Victoria and NSW respectively. O₃ and PM₂.₅ remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.