Response of atmospheric composition to COVID-19 lockdown measures during spring in the Paris region (France)

Since early 2020, the COVID-19 pandemic has led to lockdowns at national scales. These lockdowns resulted in large cuts of atmospheric pollutant emissions, notably related to the vehicular traffic source, especially during spring 2020. As a result, air quality changed in manners that are still currently under investigation. The robust quantitative assessment of the impact of lockdown measures on ambient concentrations is however hindered by weather variability. In order to circumvent this difficulty, an innovative methodology has been developed. The Analog Application for Air Quality (A3Q) method is based on the comparison of each day of lockdown to a group of analog days having similar meteorological conditions. The A3Q method has been successfully evaluated and applied to a comprehensive in situ dataset of primary and secondary pollutants obtained at the SIRTA observatory, a suburban background site of the megacity of Paris (France). The overall slight decrease of submicron particulate matter (PM1) concentrations (−14 %) compared to business-as-usual conditions conceals contrasting behaviors. Primary traffic tracers (NOx and traffic-related carbonaceous aerosols) dropped by 42 %–66 % during the lockdown period. Further, the A3Q method enabled us to characterize changes triggered by NOx decreases. Particulate nitrate and secondary organic aerosols (SOAs), two of the main springtime aerosol components in northwestern Europe, decreased by −45 % and −25 %, respectively. A NOx relationship emphasizes the interest of NOx mitigation policies at the regional (i.e., city) scale, although long-range pollution advection sporadically overcompensated for regional decreases. Variations of the oxidation state of SOA suggest discrepancies in SOA formation processes. At the same time, the expected ozone increase (+20 %) underlines the negative feedback of NO titration. These results provide a quasi-comprehensive observation-based insight for mitigation policies regarding air quality in future low-carbon urban areas.

Influence of atmospheric in-cloud aqueous-phase chemistry on the global simulation of SO₂ in CESM2

Sulfur dioxide (SO2) is a major atmospheric pollutant and precursor of sulfate aerosols, which influences air quality, cloud microphysics, and climate. Therefore, better understanding the conversion of SO2 to sulfate is essential to simulate and predict sulfur compounds more accurately. This study evaluates the effects of in-cloud aqueous-phase chemistry on SO2 oxidation in the Community Earth System Model version 2 (CESM2). We replaced the default parameterized SO2 aqueous-phase reactions with detailed HOx, Fe, N, and carbonate chemistry in cloud droplets and performed a global simulation for 2014–2015. Compared with the observations, the results incorporating detailed cloud aqueous-phase chemistry greatly reduced SO2 overestimation. This overestimation was reduced by 0.1–10 ppbv (parts per billion by volume) in most of Europe, North America, and Asia and more than 10 ppbv in parts of China. The biases in annual simulated SO2 mixing ratios decreased by 46 %, 41 %, and 22 % in Europe, the USA, and China, respectively. Fe chemistry and HOx chemistry contributed more to SO2 oxidation than N chemistry. Higher concentrations of soluble Fe and higher pH values could further enhance the oxidation capacity. This study emphasizes the importance of detailed in-cloud aqueous-phase chemistry for the oxidation of SO2. These mechanisms can improve SO2 simulation in CESM2 and deepen understanding of SO2 oxidation and sulfate formation.

A Gaussian Process Method with Uncertainty Quantification for Air Quality Monitoring

The monitoring and forecasting of particulate matter (e.g., PM2.5) and gaseous pollutants (e.g., NO, NO2, and SO2) is of significant importance, as they have adverse impacts on human health. However, model performance can easily degrade due to data noises, environmental and other factors. This paper proposes a general solution to analyse how the noise level of measurements and hyperparameters of a Gaussian process model affect the prediction accuracy and uncertainty, with a comparative case study of atmospheric pollutant concentrations prediction in Sheffield, UK, and Peshawar, Pakistan. The Neumann series is exploited to approximate the matrix inverse involved in the Gaussian process approach. This enables us to derive a theoretical relationship between any independent variable (e.g., measurement noise level, hyperparameters of Gaussian process methods), and the uncertainty and accuracy prediction. In addition, it helps us to discover insights on how these independent variables affect the algorithm evidence lower bound. The theoretical results are verified by applying a Gaussian processes approach and its sparse variants to air quality data forecasting.

Global Surface HCHO Distribution Derived from Satellite Observations with Neural Networks Technique

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants in outdoor air. However, the lack of monitoring of the global surface concentration of HCHO is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or, for research on a global scale, too data-demanding. To alleviate this issue, we adopted neural networks to estimate the 2019 global surface HCHO concentration with confidence intervals, utilizing HCHO vertical column density data from TROPOMI, and in-situ data from HAPs (harmful air pollutants) monitoring networks and the ATom mission. Our results show that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentrations in the Amazon Basin, Northern China, South-east Asia, the Bay of Bengal, and Central and Western Africa are among the highest. The results from our study provide the first dataset on global surface HCHO concentration. In addition, the derived confidence intervals of surface HCHO concentration add an extra layer of confidence to our results. As a pioneering work in adopting confidence interval estimation to AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper paves the way for rigorous study of global ambient HCHO health risk and economic loss, thus providing a basis for pollution control policies worldwide.

Performance And Exhaust Emission Characteristics Investigation of Compression Ignition Engine Fuelled With Microalgae Biodiesel And Its Diesel Blends

Biofuels extracted from plant biomass can be used as fuel in CI engines to lower a hazardous atmospheric pollutant and mitigate climate risks. Furthermore, its implementation is hampered by inevitable obstacles such as feed stocks and the crop area required for their cultivation, leading to a lack of agricultural land for the expansion of food yields. Despite this, microalgae have been discovered to be the most competent and unwavering source of biodiesel due to their distinguishing characteristics of being non-eatable and requiring no cropland for cultivation. The objectives of this paper was to look into the potential of a novel, formerly underappreciated biodiesel from microalgae species which could be used as a fuel substitute. Transesterification is being used to extract the biodiesel. Microalgae are blended with petroleum diesel in percentage to create Microalgae Blends (MAB) as needed for experimentation. The impact of biodiesel on performance as well as exhaust emission attributes of a 1-cylinder diesel engine was experimentally studied. Compared to petroleum diesel, different blend of Microalgae biodiesel showed a decline in torque and hence brake power, resulting in an average fall of 7.14 percent in brake thermal efficiency and 11.54 percent increase in brake specific fuel consumption. There were wide differences in exhaust emission characteristics, including carbon monoxide and hydrocarbon, as the blend ratio in diesel increased. Moreover, nitrogen oxides and carbon dioxides increase in all algae biodiesel blends, but they're still within the acceptable range of petroleum diesel.

Short-term associations of air pollution and meteorological variables on the incidence and severity of COVID-19 in Madrid (Spain): a time series study

Background There are studies that analyze the role of meteorological variables on the incidence and severity of COVID-19, and others that explore the role played by air pollutants, but currently there are very few studies that analyze the impact of both effects together. This is the aim of the current study. We analyzed data corresponding to the period from February 1 to May 31, 2020 for the City of Madrid. As meteorological variables, maximum daily temperature (Tmax) in ºC and mean daily absolute humidity (AH) in g/m3 were used corresponding to the mean values recorded by all Spanish Meteorological Agency (AEMET) observatories in the Madrid region. Atmospheric pollutant data for PM10 and NO2 in µg/m3 for the Madrid region were provided by the Spanish Environmental Ministry (MITECO). Daily incidence, daily hospital admissions per 100.000 inhabitants, daily ICU admissions and daily death rates per million inhabitants were used as dependent variables. These data were provided by the ISCIII Spanish National Epidemiology Center. Generalized linear models with Poisson link were performed between the dependent and independent variables, controlling for seasonality, trend and the autoregressive nature of the series. Results The results of the single-variable models showed a negative association between Tmax and all of the dependent variables considered, except in the case of deaths, in which lower temperatures were associated with higher rates. AH also showed the same behavior with the COVID-19 variables analyzed and with the lags, similar to those obtained with Tmax. In terms of atmospheric pollutants PM10 and NO2, both showed a positive association with the dependent variables. Only PM10 was associated with the death rate. Associations were established between lags 12 and 21 for PM10 and between 0 and 28 for NO2, indicating a short-term association of NO2 with the disease. In the two-variable models, the role of NO2 was predominant compared to PM10. Conclusions The results of this study indicate that the environmental variables analyzed are related to the incidence and severity of COVID-19 in the Community of Madrid. In general, low temperatures and low humidity in the atmosphere affect the spread of the virus. Air pollution, especially NO2, is associated with a higher incidence and severity of the disease. The impact that these environmental factors are small (in terms of relative risk) and by themselves cannot explain the behavior of the incidence and severity of COVID-19.

Global Surface HCHO Distribution derived from Satellite Observations with Neural Networks Technique

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or data- demanding for a global scale of research. To alleviate this issue, we adopted neural networks to estimate surface HCHO concentration with confidence intervals in 2019, where HCHO vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, Northern China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. The results from our study provides a first dataset of the global surface HCHO concentration. In addition, the derived confidence interval of surface HCHO concentration adds an extra layer for the confidence to our results. As a pioneer work in adopting confidence interval estimation into AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper will pave the way for the rigorous study on global ambient HCHO health risk and economic loss, thus providing a basis for pollutant controlling policies worldwide.

Source-Resolved Variability of Fine Particulate Matter and Human Exposure in an Urban Area

Increasing the resolution of chemical transport model (CTM) predictions in urban areas is important to capture sharp spatial gradients in atmospheric pollutant concentrations and better inform air quality and emissions controls policies that protect public health. The chemical transport model PMCAMx was used to assess the impact of increasing model resolution on the ability to predict the source-resolved variability and population exposure to PM2.5 at 36 x 36, 12 x 12, 4 x 4, and 1 x 1 km resolutions over the city of Pittsburgh during typical winter and summer periods (February and July 2017). At the coarse resolution, county-level differences can be observed, while increasing the resolution to 12 x 12 km resolves the urban-rural gradient. Increasing resolution to 4 x 4 km resolves large stationary sources such as power plants and the 1 x 1 km resolution reveals intra-urban variations and individual roadways within the simulation domain. Regional pollutants that exhibit low spatial variability such as PM2.5 nitrate show modest changes when increasing the resolution beyond 12 x 12 km. Predominantly local pollutants such as elemental carbon and organic aerosol have gradients that can only be resolved at the 1 x 1 km scale. Contributions from some local sources are enhanced by weighting the average contribution from each source by the population in each grid cell. The average population weighted PM2.5 concentration does not change significantly with resolution, suggesting that extremely high resolution PM2.5 predictions may not be necessary for effective urban epidemiological analysis.