Predicting the effect of confinement on the COVID-19 spread using machine learning enriched with satellite air pollution observations.

The real-time monitoring of reductions of economic activity by containment measures and its effect on the transmission of the coronavirus (COVID-19) is a critical unanswered question. We inferred 5,642 weekly activity anomalies from the meteorology-adjusted differences in spaceborne tropospheric NO2 column concentrations after the 2020 COVID-19 outbreak relative to the baseline from 2016 to 2019. Two satellite observations reveal reincreasing economic activity associated with lifting control measures that comes together with accelerating COVID-19 cases before the winter of 2020/2021. Application of the near-real-time satellite NO2 observations produces a much better prediction of the deceleration of COVID-19 cases than applying the Oxford Government Response Tracker, the Public Health and Social Measures, or human mobility data as alternative predictors. A convergent cross-mapping suggests that economic activity reduction inferred from NO2 is a driver of case deceleration in most of the territories. This effect, however, is not linear, while further activity reductions were associated with weaker deceleration. Over the winter of 2020/2021, nearly 1 million daily COVID-19 cases could have been avoided by optimizing the timing and strength of activity reduction relative to a scenario based on the real distribution. Our study shows how satellite observations can provide surrogate data for activity reduction during the COVID-19 pandemic and monitor the effectiveness of containment to the pandemic before vaccines become widely available.

Evolution of Ozone Pollution in China: What Track Will It Follow?

Increasing surface ozone (O3) concentrations has emerged as a key air pollution problem in many urban regions worldwide in the last decade. A longstanding major issue in tackling ozone pollution is the identification of the O3 formation regime and its sensitivity to precursor emissions. In this work, we propose a new transformed empirical kinetic modeling approach (EKMA) to diagnose the O3 formation regime using regulatory O3 and NO2 observation datasets, which are easily accessible. We demonstrate that mapping of monitored O3 and NO2 data on the modeled regional O3-NO2 relationship diagram can illustrate the ozone formation regime and historical evolution of O3 precursors of the region. By applying this new approach, we show that for most urban regions of China, the O3 formation is currently associated with a volatile organic compound (VOC)-limited regime, which is located within the zone of daytime-produced O3 (DPO3) to an 8h-NO2 concentration ratio below 8.3 ([DPO3]/[8h-NO2] ≤ 8.3). The ozone production and controlling effects of VOCs and NOx in different cities of China were compared according to their historical O3-NO2 evolution routes. The approach developed herein may have broad application potential for evaluating the efficiency of precursor controls and further mitigating O3 pollution, in particular, for regions where comprehensive photochemical studies are unavailable.

Separating Daily 1 km PM2.5 Inorganic Chemical Composition in China since 2000 via Deep Learning Integrating Ground, Satellite, and Model Data.

Fine particulate matter (PM2.5) chemical composition has strong and diverse impacts on the planetary environment, climate, and health. These effects are still not well understood due to limited surface observations and uncertainties in chemical model simulations. We developed a four-dimensional spatiotemporal deep forest (4D-STDF) model to estimate daily PM2.5 chemical composition at a spatial resolution of 1 km in China since 2000 by integrating measurements of PM2.5 species from a high-density observation network, satellite PM2.5 retrievals, atmospheric reanalyses, and model simulations. Cross-validation results illustrate the reliability of sulfate (SO42-), nitrate (NO3-), ammonium (NH4+), and chloride (Cl-) estimates, with high coefficients of determination (CV-R2) with ground-based observations of 0.74, 0.75, 0.71, and 0.66, and average root-mean-square errors (RMSE) of 6.0, 6.6, 4.3, and 2.3 µg/m3, respectively. The three components of secondary inorganic aerosols (SIAs) account for 21% (SO42-), 20% (NO3-), and 14% (NH4+) of the total PM2.5 mass in eastern China; we observed significant reductions in the mass of inorganic components by 40-43% between 2013 and 2020, slowing down since 2018. Comparatively, the ratio of SIA to PM2.5 increased by 7% across eastern China except in Beijing and nearby areas, accelerating in recent years. SO42- has been the dominant SIA component in eastern China, although it was surpassed by NO3- in some areas, e.g., Beijing-Tianjin-Hebei region since 2016. SIA, accounting for nearly half (∼46%) of the PM2.5 mass, drove the explosive formation of winter haze episodes in the North China Plain. A sharp decline in SIA concentrations and an increase in SIA-to-PM2.5 ratios during the COVID-19 lockdown were also revealed, reflecting the enhanced atmospheric oxidation capacity and formation of secondary particles.

The Response in Air Quality to the Reduction of Chinese Economic Activities During the COVID-19 Outbreak.

During the COVID-19 outbreak that took place in early 2020, the economic activities in China were drastically reduced and accompanied by a strong reduction in the emission of primary air pollutants. On the basis of measurements made at the monitoring stations operated by the China National Environmental Monitoring Center, we quantify the reduction in surface PM2.5, NO2, CO, and SO2 concentrations in northern China during the lockdown, which started on 23 January 2020. We find that, on the average, the levels of surface PM2.5 and NO2 have decreased by approximately 35% and 60%, respectively, between the period 1 and 22 January 2020 and the period 23 January and 29 February 2020. At the same time, the mean ozone concentration has increased by a factor 1.5-2. In urban area of Wuhan, where drastic measures were adopted to limit the spread of the coronavirus, similar changes in the concentrations of PM2.5, NO2, and ozone are found.

Heatwave exacerbates air pollution in China through intertwined climate-energy-environment interactions.

Climate change is increasing the frequency and intensity of heatwaves, raising concerns about their detrimental effects on air quality. However, a role for heatwave-human-environment interactions in air pollution exacerbation has not been established. In the summer of 2022, record-breaking heatwaves struck China and Europe. In this study, we use integrated observational data and machine learning to elucidate the formation mechanism underlying one of the most severe ozone pollution seasons on record in central eastern China, an area that encompasses approximately half of China's total population and sown land. Our findings reveal that the worsened ozone and nitrogen dioxide pollution resulted from a mismatch between energy demand and supply, which was driven by both heatwaves and energy policy-related factors. The observed adverse heatwave-energy-environment feedback loop highlights the need for the diversification of clean energy sources, more resilient energy structures and power policies, and further emission control to confront the escalating climate challenge in the future.

The effect of cross-regional transport on ozone and particulate matter pollution in China: A review of methodology and current knowledge.

China is currently one of the countries impacted by severe atmospheric ozone (O3) and particulate matter (PM) pollution. Due to their moderately long lifetimes, O3 and PM can be transported over long distances, cross the boundaries of source regions and contribute to air pollution in other regions. The reported contributions of cross-regional transport (CRT) to O3 and fine PM (PM2.5) concentrations often exceed those of local emissions in the major regions of China, highlighting the important role of CRT in regional air pollution. Therefore, further improvement of air quality in China requires more joint efforts among regions to ensure a proper reduction in emissions while accounting for the influence of CRT. This review summarizes the methodologies employed to assess the influence of CRT on O3 and PM pollution as well as current knowledge of CRT influence in China. Quantifying CRT contributions in proportion to O3 and PM levels and studying detailed CRT processes of O3, PM and precursors can be both based on targeted observations and/or model simulations. Reported publications indicate that CRT contributes by 40-80 % to O3 and by 10-70 % to PM2.5 in various regions of China. These contributions exhibit notable spatiotemporal variations, with differences in meteorological conditions and/or emissions often serving as main drivers of such variations. Based on trajectory-based methods, transport pathways contributing to O3 and PM pollution in major regions of China have been revealed. Recent studies also highlighted the important role of horizontal transport in the middle/high atmospheric boundary layer or low free troposphere, of vertical exchange and mixing as well as of interactions between CRT, local meteorology and chemistry in the detailed CRT processes. Drawing on the current knowledge on the influence of CRT, this paper provides recommendations for future studies that aim at supporting ongoing air pollution mitigation strategies in China.

Significant chlorine emissions from biomass burning affect the long-term atmospheric chemistry in Asia.

Biomass burning (BB) is a major source of trace gases and particles in the atmosphere, influencing air quality, radiative balance, and climate. Previous studies have mainly focused on the BB emissions of carbon and nitrogen species with less attention on chlorine. Reactive chlorine chemistry has significant effects on atmospheric chemistry and air quality. However, quantitative information on chlorine emissions from BB, particularly the long-term trend and associated atmospheric impacts, is limited both on regional and global scales. Here, we report a long-term (2001-2018) high-resolution BB emission inventory for the major chlorine-containing compounds (HCl, chloride, and CH3Cl) in Asia based on satellite observations. We estimate an average of 730 Gg yr-1 chlorine emitted from BB activity in Asia, with China contributing the largest share at 24.2% (177 Gg yr-1), followed by Myanmar at 18.7% and India at 18.3%. Distinct seasonal patterns and significant spatial and interannual variability are observed, mainly driven by human-mediated changes in agricultural activity. By incorporating the newly developed chlorine emission inventory into a global chemistry-climate model (CAM-Chem), we find that the BB-chlorine emissions lead to elevated levels of HCl and CH3Cl (monthly average up to 2062 and 1421 parts per trillion by volume (pptv), respectively), subsequently resulting in noticeable changes in oxidants (up to 3.1% in O3 and 17% in OH radicals). The results demonstrate that BB is not only a significant source of air pollutants but also of oxidants, suggesting a larger role of BB emissions in the atmospheric chemistry and oxidation process than previously appreciated. In light of the projected increase in BB activity toward the end of the century and the extensive control of anthropogenic emissions worldwide, the contribution of BB emissions may become fundamental to air quality composition in the future.

Wavelet analysis of the atmospheric flows over real urban morphology.

Winds are the basic forces for atmospheric transport such as pollutant removal and pedestrian thermal comfort. The transport capability is commonly measured in terms of length and velocity scales. In this connection, the flows in the atmospheric surface layer (ASL) over the Kowloon Peninsula, Hong Kong (HK) are scrutinized by the large-eddy simulation (LES) to characterize the motion scales over real urban morphology. Apart from statistical analysis, the streamwise fluctuating velocity u' is examined by both wavelet and energy spectrum in which a primary peak is consistently shown at streamwise wavelength 70 m ≤ λx ≤ 300 m. A secondary peak at a longer wavelength 800 m ≤ λx ≤ 3000 m, however, is unveiled by wavelet only. It denotes the existence of intermittent turbulence structures whose sizes are much larger than those of buildings. Further wavelet analysis reveals that majority energy-carrying eddies are enlarging (tens to hundreds of meters) from the roughness sublayer (RSL) to the inertial sublayer (ISL). Analogous to its smooth-wall and schematic rough-wall counterparts, the turbulence kinetic energy (TKE) over urban areas is peaked in the ISL which is carried by eddies of size 50 m ≤ λx ≤ 1000 m. The (horizontal) spatial distribution of energy-carrying eddies is further visualized to compare the crucial motion scales in the RSL and ISL. Finally, conditional sampling is used to demystify the contribution to vertical momentum flux u'w' in terms of streamwise wavelength and quadrants. The results advance our fundamental understanding of ASL transport processes, fostering sustainable environmental policy.

Empirical mode decomposition of the atmospheric flows and pollutant transport over real urban morphology.

The momentum transport and pollutant dispersion in the atmospheric surface layer (ASL) are governed by a broad spectrum of turbulence structures. Whereas, their contributions have not been explicitly investigated in the context of real urban morphology. This paper aims to elucidate the contributions from different types of eddies in the ASL over a dense city to provide the reference of urban planning, realizing more favorable ventilation and pollutant dispersion. The building-resolved large-eddy simulation dataset of winds and pollutants over the Kowloon downtown, Hong Kong, is decomposed into a few intrinsic mode functions (IMFs) via empirical mode decomposition (EMD). EMD is a data-driven algorithm that has been successfully implemented in many research fields. The results show that four IMFs are generally enough to capture most of the turbulence structures in real urban ASL. In particular, the first two IMFs, which are initiated by individual buildings, capture the small-scale vortex packets that populate within the irregular building clusters. On the other hand, the third and fourth IMFs capture the large-scale motions (LSMs) detached to the ground surface that are highly efficient in transport. They collectively contribute to nearly 40% of vertical momentum transport even with relatively low vertical turbulence kinetic energy (TKE). LSMs are long, streaky structures that mainly consist of streamwise TKE components. It is found that the open areas and regular streets promote the portion of streamwise TKE in LSMs, improving the vertical momentum transport and pollutant dispersion. In addition, these streaky LSMs are found to play a crucial role in pollutant dilution in the near field after the pollutant source, while the small-scale vortex packets are more efficient in transport in the mid-field and far-field.

Contamination of tea leaves by anthraquinone: The atmosphere as a possible source.

The detection of anthraquinone in tea leaves has raised concerns due to a potential health risk associated with this species. This led the European Union to impose a maximum residue limit (MRL) of 0.02 mg/kg for anthraquinone in dried tea leaves. As atmospheric contamination has been identified as one of the possible sources of anthraquinone residue, this study investigates the contamination resulting from the deposition of atmospheric anthraquinone using a global chemical transport model that accounts for the emission, atmospheric transport, chemical transformation, and deposition of anthraquinone on the surface. The largest contribution to the global atmospheric budget of anthraquinone is from residential combustion followed by the secondary formation from oxidation of anthracene. Simulations suggest that atmospheric anthraquinone deposition could be a substantial source of the anthraquinone found on tea leaves in several tea-producing regions, especially near highly industrialized and populated areas of southern and eastern Asia. The high level of anthraquinone deposition in these areas may result in residues in tea products exceeding the EU MRL. Additional contamination could also result from local tea production operations.

Observed sensitivities of PM2.5 and O3 extremes to meteorological conditions in China and implications for the future.

Frequent extreme air pollution episodes in China accompanied with high concentrations of particulate matters (PM2.5) and ozone (O3) are partly supported by meteorological conditions. However, the relationships between meteorological variables and pollution extremes can be poorly estimated solely based on mean pollutant level. In this study, we use quantile regression to investigate meteorological sensitivities of PM2.5 and O3 extremes, benefiting from nationwide observations of air pollutants over 2013-2019 in China. Results show that surface winds and humidity are identified as key drivers for high PM2.5 events during both summer and winter, with greater sensitivities at higher percentiles. Higher humidity favors the hydroscopic growth of particles during winter, but it tends to decrease PM2.5 through wet scavenging during summer. Surface temperature play dominant role in summer O3 extremes, especially in VOC-limited regime, followed by surface winds and radiation. Sensitivities of O3 to meteorological conditions are relatively unchanging across percentiles. Under the fossil-fueled development pathway (SSP5-8.5) scenario, meteorological conditions are projected to favor winter PM2.5 extremes in North China Plain (NCP), Yangtze River Delta (YRD) and Sichuan Basin (SCB), mainly due to enhanced surface specific humidity. Summer O3 extremes are likely to occur more frequently in the NCP and YRD, associated with warmer temperature and stronger solar radiation. Besides, meteorological conditions over a relatively longer period play a more important role in the formation of pollution extremes. These results improve our understanding of the relationships between extreme PM2.5 and O3 pollution and meteorology, and can be used as a valuable reference of model predicted air pollution extremes.

Is atmospheric oxidation capacity better in indicating tropospheric O3 formation?

Tropospheric ozone (O3) concentration is increasing in China along with dramatic changes in precursor emissions and meteorological conditions, adversely affecting human health and ecosystems. O3 is formed from the complex nonlinear photochemical reactions from nitrogen oxides (NO x = NO + NO2) and volatile organic compounds (VOCs). Although the mechanism of O3 formation is rather clear, describing and analyzing its changes and formation potential at fine spatial and temporal resolution is still a challenge today. In this study, we briefly summarized and evaluated different approaches that indicate O3 formation regimes. We identify that atmospheric oxidation capacity (AOC) is a better indicator of photochemical reactions leading to the formation of O3 and other secondary pollutants. Results show that AOC has a prominent positive relationship to O3 in the major city clusters in China, with a goodness of fit (R 2) up to 0.6. This outcome provides a novel perspective in characterizing O3 formation and has significant implications for formulating control strategies of secondary pollutants.

From concept to action: a united, holistic and One Health approach to respond to the climate change crisis.

It is unequivocal that human influence has warmed the planet, which is seriously affecting the planetary health including human health. Adapting climate change should not only be a slogan, but requires a united, holistic action and a paradigm shift from crisis response to an ambitious and integrated approach immediately. Recognizing the urgent needs to tackle the risk connection between climate change and One Health, the four key messages and recommendations that with the intent to guide further research and to promote international cooperation to achieve a more climate-resilient world are provided.

Diverse response of surface ozone to COVID-19 lockdown in China.

Ozone (O3) is a key oxidant and pollutant in the lower atmosphere. Significant increases in surface O3 have been reported in many cities during the COVID-19 lockdown. Here we conduct comprehensive observation and modeling analyses of surface O3 across China for periods before and during the lockdown. We find that daytime O3 decreased in the subtropical south, in contrast to increases in most other regions. Meteorological changes and emission reductions both contributed to the O3 changes, with a larger impact from the former especially in central China. The plunge in nitrogen oxide (NOx) emission contributed to O3 increases in populated regions, whereas the reduction in volatile organic compounds (VOC) contributed to O3 decreases across the country. Due to a decreasing level of NOx saturation from north to south, the emission reduction in NOx (46%) and VOC (32%) contributed to net O3 increases in north China; the opposite effects of NOx decrease (49%) and VOC decrease (24%) balanced out in central China, whereas the comparable decreases (45-55%) in these two precursors contributed to net O3 declines in south China. Our study highlights the complex dependence of O3 on its precursors and the importance of meteorology in the short-term O3 variability.

Global Changes in Secondary Atmospheric Pollutants During the 2020 COVID-19 Pandemic.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in different parts of the world in response to modified emissions of primary pollutants during the COVID-19 pandemic. We quantify the respective effects of the reductions in NOx and in volatile organic carbon (VOC) emissions, which, in most cases, affect oxidants in opposite ways. Using model simulations, we show that the level of NOx has been reduced by typically 40% in China during February 2020 and by similar amounts in many areas of Europe and North America in mid-March to mid-April 2020, in good agreement with space and surface observations. We show that, relative to a situation in which the emission reductions are ignored and despite the calculated increase in hydroxyl and peroxy radicals, the ozone concentration increased only in a few NOx-saturated regions (northern China, northern Europe, and the US) during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx-controlled, the concentration of ozone decreased. SOA concentrations decrease in response to the concurrent reduction in the NOx and VOC emissions. The model also shows that atmospheric meteorological anomalies produced substantial variations in the concentrations of chemical species during the pandemic. In Europe, for example, a large fraction of the ozone increase in February 2020 was associated with meteorological anomalies, while in the North China Plain, enhanced ozone concentrations resulted primarily from reduced emissions of primary pollutants.

Modeling Study of the Particulate Matter in Lima with the WRF-Chem Model: Case Study of April 2016.

The Weather Research and Forecasting-Chemistry (WRF-Chem) model was used to develop an operational air quality forecast system for the Metropolitan Area of Lima-Callao (MALC), Peru, that is affected by high particulate matter concentrations episodes. In this work, we describe the implementation of an operational air quality-forecasting platform to be used in the elaboration of public policies by decision makers, and as a research tool to evaluate the formation and transport of air pollutants in the MALC. To examine the skills of this new system, an air pollution event in April 2016 exhibiting unusually elevated PM2.5 concentrations was simulated and compared against in situ air quality measurements. In addition, a Model Output Statistic (MOS) algorithm has been developed to improve outputs of inhalable particulate matter (PM10) and fine particulate matter (PM2.5) from the WRF-Chem model. The obtained results showed that MOS increased the accuracy in terms of mean normalized bias for PM10 and PM2.5 from -43.1% and 71.3% to 3.1%, 7.3%, respectively. In addition, the mean normalized gross error for PM10 and PM2.5 were reduced from 48% and 92.3% to 13.4% and 10.1%, respectively. The WRF-Chem Model results showed an appropriate relationship between of temperature and relative humidity with observations during April 2016. Mean normalized bias for temperature and relative humidity were approximately - 0.6% and 1.1% respectively. In addition, the mean normalized gross error for temperature and relative humidity were approximately 4.0% and 0.1% respectively. The results showed that this modelling system can be a useful tool for the analysis of air quality in MALC.