High concentrations of ground-level ozone affect human health, plants, and animals. Reducing ozone pollution in rural regions, where local emissions are already low, poses challenge. We use meteorological back-trajectories, air quality model sensitivity analysis, and satellite remote sensing data to investigate the ozone sources in Yuma, Arizona and find strong international influences from Northern Mexico on 12 out of 16 ozone exceedance days. We find that such exceedances could not be mitigated by reducing emissions in Arizona; complete removal of state emissions would reduce the maximum daily 8-h average (MDA8) ozone in Yuma by only 0.7% on exceeding days. In contrast, emissions in Mexico are estimated to contribute to 11% of the ozone during these exceedances, and their reduction would reduce MDA8 ozone in Yuma to below the standard. Using satellite-based remote sensing measurements, we find that emissions of nitrogen oxides (NOₓ, a key photochemical precursor of ozone) increase slightly in Mexico from 2005 to 2016, opposite to decreases shown in the bottom-up inventory. In comparison, a decrease of NOₓ emissions in the US and meteorological factors lead to an overall of summer mean and annual MDA8 ozone in Yuma (by ∼1–4% and ∼3%, respectively). Analysis of meteorological back-trajectories also shows similar transboundary transport of ozone at the US-Mexico border in California and New Mexico, where strong influences from Northern Mexico coincide with 11 out of 17 and 6 out of 8 ozone exceedances. 2020 is the final year of the U.S.-Mexico Border 2020 Program, which aimed to reduce pollution at border regions of the US and Mexico. Our results indicate the importance of sustaining a substantial cooperative program to improve air quality at the border area.

Atmospheric aerosols play a crucial role in climate change, especially in the Himalayas and Tibetan Plateau. Here, we present the seasonal and diurnal characteristics of aerosol vertical profiles measured using a Mie lidar, along with surface black carbon (BC) measurements, at Mt. Qomolangma (QOMS), in the central Himalayas, in 2018–2019. Lidar-retrieved profiles of aerosols showed a distinct seasonal pattern of aerosol loading (aerosol extinction coefficient, AEC), with a maximum in the pre-monsoon (19.8 ± 22.7 Mm⁻¹ of AEC) and minimum in the summer monsoon (7.0 ± 11.2 Mm⁻¹ of AEC) seasons. The diurnal variation characteristics of AEC and BC were quite different in the non-monsoon seasons with enriched aerosols being maintained from 00:00 to 10:00 in the pre-monsoon season. The major aerosol types at QOMS were identified as background, pollution, and dust aerosols, especially during the pre-monsoon season. The occurrence of pollution events influenced the vertical distribution, seasonal/diurnal patterns, and types of aerosols. Source contribution of BC based on the weather research and forecasting chemical model showed that approximately 64.2% ± 17.0% of BC at the QOMS originated from India and Nepal in South Asia during the non-monsoon seasons, whereas approximately 47.7% was from local emission sources in monsoon season. In particular, the high abundance of BC at the QOMS in the pre-monsoon season was attributed to biomass burning, whereas anthropogenic emissions were the likely sources during the other seasons. The maximum aerosol concentration appeared in the near-surface layer (approximately 4.3 km ASL), and high concentrations of transported aerosols were mainly found at 4.98, 4.58, 4.74, and 4.88 km ASL in the pre-monsoon, monsoon, post-monsoon, and winter seasons, respectively. The investigation of the vertical profiles of aerosols at the QOMS can help verify the representation of aerosols in the air quality model and satellite products and regulate the anthropogenic disturbance over the Tibetan Plateau.

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.

High particulate matter (PM) and ozone (O₃) concentration in Hong Kong are frequently observed during the summertime typhoon season. Despite the critical effect of a typhoon on air pollution, contributions of vertical wind profile and cloud movement during transboundary air pollution (TAP) on surface PM and O₃ concentration have yet to be fully understood. This work is the first study to apply a network of Doppler light detection and ranging (LiDAR) as well as back trajectory analysis to comprehensively analyze the effect of a weak Typhoon (Danas) occurring during 16–19 July 2019 on different variations in PM and O₃ concentration. During the typhoon Danas, three types of surface air pollution with five episodes were identified: (1) low PM and high O₃ concentration; (2) co-occurring high PM and O₃ concentration and (3) high PM and low O₃ concentration. Employing our 3D Real-Time Atmospheric Monitoring System (3DREAMs) along with surface observations, we found the important role of TAP in the increases in surface PM and O₃ concentration with significant vertical wind shear that transported air pollutants at upper levels, and strong vertical mixing that brought air pollutants to the ground level. Cloud movement related to typhoon periphery, as well as high solar radiation due to sinking motion and remote transport by continental wind, have an impact on local O₃ concentration. For the substantial difference in O₃ concentration between two air quality measurement sites, the similar vertical aerosol distributions and wind profiles suggest the comparable TAP contributions at the two sites and thus infer the critical role of local O₃ photochemical process in the O₃ difference. This work comprehensively reveals the influences of a weak typhoon on variations in PM and O₃ during the five episodes, providing important references for air quality monitoring and forecast in regions under the influence of typhoon.

Vehicular emissions contribute significantly to air pollution, and the number of vehicles in use is continuing to rise. Policymakers thus need to formulate vehicular emission reduction policies to improve urban air-quality. This study used different vehicle control scenarios to predict the associated potential of mitigating carbon monoxide (CO), volatile organic compounds (VOCs), nitrogen oxide (NOₓ), particulate matter with an aerodynamic diameter less than 2.5 μm (PM₂.₅), and particulate matter with an aerodynamic diameter less than 10 μm (PM₁₀) in Xi’an China, in 2020 and 2025. One business-as-usual scenario and six control scenarios were established, and vehicular emission inventory was developed according to each scenario. The results revealed that eliminating high-emission vehicles and optimizing after-treatment devices would effectively reduce vehicular emissions. In addition, increasing the number of alternative fuel vehicles, restraining vehicle use, and restraining the growth of the vehicle population would all have certain effects on CO and VOCs emissions, but the effects would not be significant for NOx, PM₂.₅, and PM₁₀. The results also indicated that if all control measures were stringently applied together, emissions of CO, VOCs, NOₓ, PM₂.₅, and PM₁₀ would be reduced by 51.66%, 51.58%, 30.19%,71.12%, and 71.81% in 2020, and 53.55%, 51.44%, 19.09%, 54.88%, and 55.51%, in 2025, respectively.

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.

Ambient fine particulate matter (PM₂.₅) has been ranked as the sixth leading risk factor globally for death and disability. Modelling methods based on having access to a limited number of monitor stations are required for capturing PM₂.₅ spatial and temporal continuous variations with a sufficient resolution. This study utilized a land use regression (LUR) model with machine learning to assess the spatial-temporal variability of PM₂.₅. Daily average PM₂.₅ data was collected from 73 fixed air quality monitoring stations that belonged to the Taiwan EPA on the main island of Taiwan. Nearly 280,000 observations from 2006 to 2016 were used for the analysis. Several datasets were collected to determine spatial predictor variables, including the EPA environmental resources dataset, a meteorological dataset, a land-use inventory, a landmark dataset, a digital road network map, a digital terrain model, MODIS Normalized Difference Vegetation Index (NDVI) database, and a power plant distribution dataset. First, conventional LUR and Hybrid Kriging-LUR were utilized to identify the important predictor variables. Then, deep neural network, random forest, and XGBoost algorithms were used to fit the prediction model based on the variables selected by the LUR models. Data splitting, 10-fold cross validation, external data verification, and seasonal-based and county-based validation methods were used to verify the robustness of the developed models. The results demonstrated that the proposed conventional LUR and Hybrid Kriging-LUR models captured 58% and 89% of PM₂.₅ variations, respectively. When XGBoost algorithm was incorporated, the explanatory power of the models increased to 73% and 94%, respectively. The Hybrid Kriging-LUR with XGBoost algorithm outperformed the other integrated methods. This study demonstrates the value of combining Hybrid Kriging-LUR model and an XGBoost algorithm for estimating the spatial-temporal variability of PM₂.₅ exposures.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

The characteristics and transport pathways of air masses vary during heavy pollution episodes (HPEs). Three categories of HPEs have been defined: HPE Ι, II, and III, corresponding to HPE durations of 1, 2, and at least 3 days, respectively. Sixty HPEs were investigated in this study. The number of HPEs decreased from 2013 to 2017 and then increased from 2017 to 2019, dominated by emission reductions and meteorological conditions. The average and maximum PM₂.₅ (i.e., aerodynamic diameter of <2.5 μm) concentrations during those HPEs in 2019 decreased by 5.6%–11.8% and 11.9%–38.5%, respectively, compared with those in 2013. The longer the duration of an HPE, the higher the PM₂.₅ concentration. Secondary inorganic aerosol concentrations and their contents in PM₂.₅ during HPE Ⅲ were found to be higher than those during HPEs Ι and Ⅱ, as secondary transformations of precursor gases are more intense during long-term HPEs. The dominant trajectories of airflow arriving in Jinan originated from the southern and southeastern regions during HPEs, realized using the Hybrid Single Particle Lagrangian Integrated Trajectory. The trajectories from the north and west of Jinan contained the highest PM₂.₅ concentrations of 323.3–432.1 μg/m³ during HPE Ⅲ, although these trajectories only contributed 5.6%–11.1% of the total dominant transport pathways, while those in trajectories from the northwest were highest during HPEs Ι and Ⅱ. The highest contributions of air masses from short distances were found during HPE Ⅲ, of 77.8%, while they were only 65.6% and 47.8% during HPEs Ι and II, respectively. More attention should be given to transport pathways within the short distance from Jinan. Therefore, enhancing regional cooperation in Jinan and surrounding regions (particularly in the south, southeast, northwest, west, and north) is critical for improving air quality in the North China Plain.

Indoor air quality is critically important to the human as people spend most time indoors. Indoor PM₂.₅ is related to the outdoor levels, but more directly influenced by internal sources. Severe household air pollution from solid fuel use has been recognized as one major risk for human health especailly in rural area, however, the issue is significantly overlooked in most national air quality controls and intervention policies. Here, by using low-cost sensors, indoor PM₂.₅ in rural homes burning coals was monitored for ~4 months and analyzed for its temporal dynamics, distributions, relationship with outdoor PM₂.₅, and quantitative contributions of internal sources. A bimodal distribution of indoor PM₂.₅ was identified and the bimodal characteristic was more significant at the finer time resolution. The bimodal distribution maxima were corresponding to the emissions from strong internal sources and the influence of outdoor PM₂.₅, respectively. Indoor PM₂.₅ was found to be correlated with the outdoor PM₂.₅, even though indoor coal combustion for heating was thought to be predominant source of indoor PM₂.₅. The indoor-outdoor relationship differed significantly between the heating and non-heating seasons. Impacts of typical indoor sources like cooking, heating associated with coal use, and smoking were quantitatively analyzed based on the highly time-resolved PM₂.₅. Estimated contribution of outdoor PM₂.₅ to the indoor PM₂.₅ was ~48% during the non-heating period, but decreased to about 32% during the heating period. The contribution of indoor heating burning coals comprised up to 47% of the indoor PM₂.₅ during the heating period, while the other indoor sources contributed to ~20%. The study, based on a relatively long-term timely resolved PM₂.₅ data from a large number of rural households, provided informative results on temporal dynamics of indoor PM₂.₅ and quantitative contributions of internal sources, promoting scientific understanding on sources and impacts of household air pollution.