The state of science on severe air pollution episodes: Quantitative and qualitative analysis

Future fire-smoke PM2.5 health burden under climate change in Paraguay

Recent years have seen a rise in wildfire and extreme weather activity across the globe, which is projected to keep increasing with climate-induced conditions. Air pollution, especially fine particulate matter (PM2.5) concentration, is heavily affected by PM2.5 emissions from wildfire activity. Paraguay has been historically suffering from fires, with an average of 2.3 million hectares burnt per year during the 2003-2021 period. Annual PM2.5 concentration in Paraguay is 13.2 μg/m3, more than double the recommended by the WHO. We estimate that, historically, almost 40 % of fine air particulates can be attributed to fires. Using a random forest algorithm, we estimate future fire activity and fire related PM2.5 under different climate change scenarios. With global warming, we calculate that fire activity could increase by up to 120 % by 2100. Annual fire smoke PM2.5 from fires is expected to increase by 7.7 μg/m3 by 2100. Under these conditions, Paraguay is expected to suffer an increase in 3500 deaths per year attributable to fire smoke PM2.5 by 2100. We estimate the economic cost of fire smoke-related mortality by 2100 at US $ 5600 million, equivalent to 2.6 % of Paraguay's GDP, excluding other health- and productivity-related impacts on society.

Development and application of a multi-task oriented deep learning model for quantifying drivers of air pollutant variations: A case study in Taiyuan, China

Quantitative assessment of the drivers behind the variation of six criteria pollutants, namely fine particulate matter (PM2.5), ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter (PM10), and carbon monoxide (CO), in the warming climate will be critical for subsequent decision-making. Here, a novel hybrid model of multi-task oriented CNN-BiLSTM-Attention was proposed and performed in Taiyuan during 2015-2020 to synchronously and quickly quantify the impact of anthropogenic and meteorological factors on the six criteria pollutants variations. Empirical results revealed the residential and transportation sectors distinctly decreased SO2 by 25 % and 22 % and CO by 12 % and 10 %. Gradual downward trends for PM2.5, PM10, and NO2 were mainly ascribed to the stringent measures implemented in transportation and power sectors as part of the Blue Sky Defense War, which were further reinforced by the COVID-19 pandemic. Nevertheless, temperature-dependent adverse meteorological effects (27 %) and anthropogenic intervention (12 %) jointly increased O3 by 39 %. The O3-driven pollution events may be inevitable or even become more prominent under climate warming. The industrial (5 %) and transportation sectors (6 %) were mainly responsible for the anthropogenic-driven increase of O3 and precursor NO2, respectively. Synergistic reduction of precursors (VOCs and NOx) from industrial and transportation sectors requires coordination with climate actions to mitigate the temperature-dependent O3-driven pollution, thereby improving regional air quality. Meanwhile, the proposed model is expected to be applied flexibly in various regions to quantify the drivers of the pollutant variations in a warming climate, with the potential to offer valuable insights for improving regional air quality in near future.

Monthly average air pollution models using geographically weighted regression in Europe from 2000 to 2019

Detailed spatial models of monthly air pollution levels at a very fine spatial resolution (25 m) can help facilitate studies to explore critical time-windows of exposure at intermediate term. Seasonal changes in air pollution may affect both levels and spatial patterns of air pollution across Europe. We built Europe-wide land-use regression (LUR) models to estimate monthly concentrations of regulated air pollutants (NO2, O3, PM10 and PM2.5) between 2000 and 2019. Monthly average concentrations were collected from routine monitoring stations. Including both monthly-fixed and -varying spatial variables, we used supervised linear regression (SLR) to select predictors and geographically weighted regression (GWR) to estimate spatially-varying regression coefficients for each month. Model performance was assessed with 5-fold cross-validation (CV). We also compared the performance of the monthly LUR models with monthly adjusted concentrations. Results revealed significant monthly variations in both estimates and model structure, particularly for O3, PM10, and PM2.5. The 5-fold CV showed generally good performance of the monthly GWR models across months and years (5-fold CV R2: 0.31-0.66 for NO2, 0.4-0.79 for O3, 0.4-0.78 for PM10, 0.46-0.87 for PM2.5). Monthly GWR models slightly outperformed monthly-adjusted models. Correlations between monthly GWR model were generally moderate to high (Pearson correlation >0.6). In conclusion, we are the first to develop robust monthly LUR models for air pollution in Europe. These monthly LUR models, at a 25 m spatial resolution, enhance epidemiologists to better characterize Europe-wide intermediate-term health effects related to air pollution, facilitating investigations into critical exposure time windows in birth cohort studies.

Global population exposure to landscape fire air pollution from 2000 to 2019

Wildfires are thought to be increasing in severity and frequency as a result of climate change1-5. Air pollution from landscape fires can negatively affect human health4-6, but human exposure to landscape fire-sourced (LFS) air pollution has not been well characterized at the global scale7-23. Here, we estimate global daily LFS outdoor fine particulate matter (PM2.5) and surface ozone concentrations at 0.25° × 0.25° resolution during the period 2000-2019 with the help of machine learning and chemical transport models. We found that overall population-weighted average LFS PM2.5 and ozone concentrations were 2.5 µg m-3 (6.1% of all-source PM2.5) and 3.2 µg m-3 (3.6% of all-source ozone), respectively, in 2010-2019, with a slight increase for PM2.5, but not for ozone, compared with 2000-2009. Central Africa, Southeast Asia, South America and Siberia experienced the highest LFS PM2.5 and ozone concentrations. The concentrations of LFS PM2.5 and ozone were about four times higher in low-income countries than in high-income countries. During the period 2010-2019, 2.18 billion people were exposed to at least 1 day of substantial LFS air pollution per year, with each person in the world having, on average, 9.9 days of exposure per year. These two metrics increased by 6.8% and 2.1%, respectively, compared with 2000-2009. Overall, we find that the global population is increasingly exposed to LFS air pollution, with socioeconomic disparities.

Facing the challenges of air quality and health in a future climate: The Aveiro Region case study

Air pollution and climate change are the most important environmental issues for European citizens. Despite the air quality improvements achieved in recent years, with most pollutants' concentrations below the European Union legislated values, it is necessary to understand whether this will continue in the future due to expected climate changes impacts. In this context, this work tries to answer two main questions: (i) What is the relative contribution of emission source regions/activities to air quality, now and in the future, considering a climate change scenario?; and (ii) What additional policies are needed to support win-win strategies for air quality and climate mitigation and/or adaptation, at urban scale? For that, a climate and air quality modelling system, with source apportionment tools, was applied to the Aveiro Region, in Portugal. Main results show that in the future, due to the implementation of carbon neutrality measures, air quality in the Aveiro Region may improve, with reduction up to 4 μg.m^-3 for particulate matter (PM) concentrations and 22 μg.m^-3 for nitrogen dioxide (NO₂), and consequently, the premature deaths due to air pollution exposure will also decrease. The expected air quality improvement will ensure that, in the future, the limit values of the European Union (EU) Air Quality Directive will not be exceeded, but the same will not happen if the proposed revision of the EU Air Quality Directive is approved. Results also shown that, in the future, industrial sector will be the one with higher relative contribution for PM concentrations and the second one for NO₂. For that sector, additional emission abatement measures were tested, showing that, in the future, it is possible to comply with all the new limit values proposed by the EU.

Trends of source apportioned PM2.5 in Tianjin over 2013-2019: Impacts of Clean Air Actions

A long-term (2013-2019) PM_2.5 speciation dataset measured in Tianjin, the largest industrial city in northern China, was analyzed with dispersion normalized positive matrix factorization (DN-PMF). The trends of source apportioned PM_2.5 were used to assess the effectiveness of source-specific control policies and measures in support of the two China's Clean Air Actions implemented nationwide in 2013-2017 and 2018-2020, respectively. Eight sources were resolved from the DN-PMF analysis: coal combustion (CC), biomass burning (BB), vehicular emissions, dust, steelmaking and galvanizing emissions, a mixed sulfate-rich factor and secondary nitrate. After adjustment for meteorological fluctuations, a substantial improvement in PM_2.5 air quality was observed in Tianjin with decreases in PM_2.5 at an annual rate of 6.6%/y. PM_2.5 from CC decreased by 4.1%/y. The reductions in SO₂ concentration, PM_2.5 contributed by CC, and sulfate demonstrated the improved control of CC-related emissions and fuel quality. Policies aimed at eliminating winter-heating pollution have had substantial success as shown by reduced heating-related SO₂, CC, and sulfate from 2013 to 2019. The two industrial source types showed sharp drops after the 2013 mandated controls went into effect to phaseout outdated iron/steel production and enforce tighter emission standards for these industries. BB reduced significantly by 2016 and remained low due to the no open field burning policy. Vehicular emissions and road/soil dust declined over the Action's first phase followed by positive upward trends, showing that further emission controls are needed. Nitrate concentrations remained constant although NO_X emissions dropped significantly. The lack of a decrease in nitrate may result from increased ammonia emissions from enhanced vehicular NO_X controls. The port and shipping emissions were evident implying their impacts on coastal air quality. These results affirm the effectiveness of the Clean Air Actions in reducing primary anthropogenic emissions. However, further emission reductions are needed to meet global health-based air quality standards.

Health risk assessment of the European inhabitants exposed to contaminated ambient particulate matter by potentially toxic elements

PM₁₀-associated potential toxic elements (PTEs) can enter the respiratory system and cause health problems. In the current study, the health risk indices caused by PM₁₀ inhalation by adults, children, and infants in 158 European cities between 2013 and 2019 were studied to determine if Europeans were adversely affected by carcinogenic and non-carcinogenic factors or not. The Mann-Kendall trend test examined PM₁₀'s increasing or decreasing trend. Random Forest analysis was also used to analyse meteorological factors affecting PM₁₀ in Europe. Hazard quotient and cancer risk were estimated using PM₁₀-associated PTEs. Our results showed a decline in continental PM₁₀ concentrations. The correlation between PM₁₀ concentrations and temperature (-0.40), PBLH (-0.39), and precipitation were statistically strong (-0.21). The estimated Pearson correlation coefficients showed a statistically strong positive correlation between As & Pb, As & Cd, and Cd & Pb during 2013-2019, indicating a similar origin. PTEs with hazard quotients below one, regardless of subpopulation type, posed no noncancerous risk to Europeans. The hazard quotient values positively correlated with time, possibly due to elevated PTE levels. In our study on carcinogen pollution in Europe between 2013 and 2019, we found unacceptable levels of As, Cd, Ni, and Pb among adults, children, and infants. Carcinogenic risk rates were highest for children, followed by infants, adult women, and adult men. Therefore, besides monitoring and mitigating PM concentrations, effective control of PM sources is also needed.

Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China

Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM_2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM_2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM_2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM_2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM_2.5. The peak time of PM_2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM_2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM_2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM_2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM_2.5 pollution, more attention needs to be paid to the nocturnal PM_2.5 not only at surface level but also at higher levels in the presence of TIs.

Spatiotemporal characteristics of air pollution in Chengdu-Chongqing urban agglomeration (CCUA) in Southwest, China: 2015-2021

Studying the spatiotemporal characteristics of air pollutants in urban agglomerations and their response factors will help to improve the quality of urban living. In combining air quality monitoring data and wavelet analysis from the Chengdu-Chongqing urban agglomeration (CCUA), this study assessed the spatiotemporal distribution characteristics and influential factors of air pollutants on daily, monthly and annual scales. The results showed that the concentration of air pollutants in the CCUA has decreased year by year, and air quality has improved. Except for O₃, pollutants in autumn and winter were higher than those in summer. The spatial distribution of air pollutants was obvious distributed in Chengdu, Chongqing, Zigong and Dazhou. Pollution incidents were mainly concentrated in winter. The 6 air pollutants and air quality index (AQI) have dominant periods on multiple time scales. AQI showed positive coherence with PM_2.5 and PM₁₀ on multiple time scales, and obvious positive coherence with SO₂, CO, NO₂ and O₃ in the short term scale. AQI was not strongly correlated with the fire point, but exhibited obvious negative coherence in the long term scale. In addition, AQI showed an obvious positive correlation with temperature and sunshine hours in short term, and a clear negative correlation with humidity and rainfall. The research results of this paper will provide a reference for pollution prevention and control in the CCUA.

Acute health effects of bushfire smoke on mortality in Sydney, Australia

BACKGROUND

Bushfire smoke is a major ongoing environmental hazard in Australia. In the summer of 2019-2020 smoke from an extreme bushfire event exposed large populations to high concentrations of particulate matter (PM) pollution. In this study we aimed to estimate the effect of bushfire-related PM of less than 2.5 μm in diameter (PM_2.5) on the risk of mortality in Sydney, Australia from 2010 to 2020.

METHODS

We estimated concentrations of PM_2.5 for three subregions of Sydney from measurements at monitoring stations using inverse-distance weighting and cross-referenced extreme days (95th percentile or above) with satellite imagery to determine if bushfire smoke was present. We then used a seasonal and trend decomposition method to estimate the Non-bushfire PM_2.5 concentrations on those days. Daily PM_2.5 concentrations above the Non-bushfire concentrations on bushfire smoke days were deemed to be Bushfire PM_2.5. We used distributed-lag non-linear models to estimate the effect of Bushfire and Non-bushfire PM_2.5 on daily counts of mortality with sub-analyses by age. These models controlled for seasonal trends in mortality as well as daily temperature, day of week and public holidays.

RESULTS

Within the three subregions, between 110 and 134 days were identified as extreme bushfire smoke days within the subregions of Sydney. Bushfire-related PM_2.5 ranged from 6.3 to 115.4 µg/m³. A 0 to 10 µg/m³ increase in Bushfire PM_2.5 was associated with a 3.2% (95% CI 0.3, 6.2%) increase in risk of all-cause death, cumulatively, in the 3 days following exposure. These effects were present in those aged 65 years and over, while no effect was observed in people under 65 years.

CONCLUSION

Bushfire PM_2.5 exposure is associated with an increased risk of mortality, particularly in those over 65 years of age. This increase in risk was clearest at Bushfire PM_2.5 concentrations up to 30 µg/m³ above background (Non-bushfire), with possible plateauing at higher concentrations of Bushfire PM_2.5.

Changes in Air Quality and Drivers for the Heavy PM2.5 Pollution on the North China Plain Pre- to Post-COVID-19

Under the clean air action plans and the lockdown to constrain the coronavirus disease 2019 (COVID-19), the air quality improved significantly. However, fine particulate matter (PM_2.5) pollution still occurred on the North China Plain (NCP). This study analyzed the variations of PM_2.5, nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO), and ozone (O₃) during 2017-2021 on the northern (Beijing) and southern (Henan) edges of the NCP. Furthermore, the drivers for the PM_2.5 pollution episodes pre- to post-COVID-19 in Beijing and Henan were explored by combining air pollutant and meteorological datasets and the weighted potential source contribution function. Results showed air quality generally improved during 2017-2021, except for a slight rebound (3.6%) in NO₂ concentration in 2021 in Beijing. Notably, the O₃ concentration began to decrease significantly in 2020. The COVID-19 lockdown resulted in a sharp drop in the concentrations of PM_2.5, NO₂, SO₂, and CO in February of 2020, but PM_2.5 and CO in Beijing exhibited a delayed decrease in March. For Beijing, the PM_2.5 pollution was driven by the initial regional transport and later secondary formation under adverse meteorology. For Henan, the PM_2.5 pollution was driven by the primary emissions under the persistent high humidity and stable atmospheric conditions, superimposing small-scale regional transport. Low wind speed, shallow boundary layer, and high humidity are major drivers of heavy PM_2.5 pollution. These results provide an important reference for setting mitigation measures not only for the NCP but for the entire world.

The effects of air pollution, meteorological parameters, and climate change on COVID-19 comorbidity and health disparities: A systematic review

Air pollutants, especially particulate matter, and other meteorological factors serve as important carriers of infectious microbes and play a critical role in the spread of disease. However, there remains uncertainty about the relationship among particulate matter, other air pollutants, meteorological conditions and climate change and the spread of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), hereafter referred to as COVID-19. A systematic review was conducted using PRISMA guidelines to identify the relationship between air quality, meteorological conditions and climate change, and COVID-19 risk and outcomes, host related factors, co-morbidities and disparities. Out of a total of 170,296 scientific publications screened, 63 studies were identified that focused on the relationship between air pollutants and COVID-19. Additionally, the contribution of host related-factors, co-morbidities, and health disparities was discussed. This review found a preponderance of evidence of a positive relationship between PM_2.5, other air pollutants, and meteorological conditions and climate change on COVID-19 risk and outcomes. The effects of PM_2.5, air pollutants, and meteorological conditions on COVID-19 mortalities were most commonly experienced by socially disadvantaged and vulnerable populations. Results however, were not entirely consistent, and varied by geographic region and study. Opportunities for using data to guide local response to COVID-19 are identified.

Mechanisms and Pathways for Coordinated Control of Fine Particulate Matter and Ozone

PURPOSE OF REVIEW

Fine particulate matter (PM_2.5) and ground-level ozone (O₃) pose a significant risk to human health. The World Health Organization (WHO) has recently revised healthy thresholds for both pollutants. The formation and evolution of PM_2.5 and O₃ are however governed by complex physical and multiphase chemical processes, and therefore, it is extremely challenging to mitigate both pollutants simultaneously. Here, we review mechanisms and discuss the science-informed pathways for effective and simultaneous mitigation of PM_2.5 and O₃.

RECENT FINDINGS

Global warming has led to a general increase in biogenic emissions, which can enhance the formation of O₃ and secondary organic aerosols. Reductions in anthropogenic emissions during the COVID-19 lockdown reduced PM_2.5; however, O₃ was enhanced in several polluted regions. This was attributed to more intense sunlight due to low aerosol loading and non-linear response of O₃ to NO _x . Such contrasting physical and chemical interactions hinder the formulation of a clear roadmap for clean air over such regions.

SUMMARY

Atmospheric chemistry including the role of biogenic emissions, aerosol-radiation interactions, boundary layer, and regional-scale transport are the key aspects that need to be carefully considered in the formulation of mitigation pathways. Therefore, a thorough understanding of the chemical effects of the emission reductions, changes in photolytic rates and boundary layer due to perturbation of solar radiation, and the effect of meteorological/seasonal changes are needed on a regional basis. Statistical emulators and machine learning approaches can aid the cumbersome process of multi-sector multi-species source attribution.