Spatio-temporal dynamics of aerosol distribution in an urban environment recorded in situ by means of a bike based monitoring system

Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00–8:00 LT) and night (19:00–22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect” was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health. © 2022 Elsevier Ltd

Air pollution and mobility in the Mexico City Metropolitan Area in times of COVID-19

This paper analyzes the relation between COVID-19, air pollution, and public transport mobility in the Mexico City Metropolitan Area (MCMA). We test if the restrictions to economic activity introduced to mitigate the spread of COVID-19 are associated with a structural change in air pollution levels and public transport mobility. Our results show that mobility in public transportation was significantly reduced following the government's recommendations. Nonetheless, we show that the reduction in mobility was not accompanied by a reduction in air pollution. Furthermore, Granger-causality tests show that the precedence relation between public transport mobility and air pollution disappeared as a product of the restrictions. Thus, our results suggest that air pollution in the MCMA seems primarily driven by industry and private car usage. In this regard, the government should redouble its efforts to develop policies to reduce industrial pollution and private car usage. © 2023 Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático. This is an open access article under the CC BY-NC License (http://creativecommons.org/licenses/by-nc/4.0/).

Using Machine Learning to estimate the impact of different modes of transport and traffic restriction strategies on urban air quality

Classical pollutant dispersion models, based on the numerical resolution of some approximate form of the momentum, energy and chemical species conservation equations, are usually limited by incomplete descriptions of the atmospheric boundary layer hydrodynamics, partial characterizations of the emission inventories and, often, high computational costs. Using the metropolitan area of Barcelona as benchmark, the Machine Learning aproach presented here alleviates these limitations providing very accurate local predictions of key pollutant concentrations. Originating mostly from Open Data sources, time-series data on road, maritime and air traffic along with meteorological records from October 2017 to June 2021, have allowed, by means of Machine Learning techniques, to create a model capable of estimating the individual contributions of each mode of transport to worsened Air Quality. Also, when used to investigate the impact of recently implemented mitigation measures, model results predict a reduction of approximately 8 μg·m−3 for CO and NOx. In contrast, O3, PM10 and SO2 are found to be unaffected. The COVID-19 lockdown provided an accidental opportunity to improve the model's robustness and predictive capability through unusually low emission rates from transportation. © 2022 The Author(s)

Haze caused by NOx oxidation under restricted residential and industrial activities in a mega city in the south of North China Plain.

The formation of secondary aerosol species, including nitrate and sulfate, induces severe haze in the North China Plain. However, despite substantial reductions in anthropogenic pollutants due to severe restriction of residential and industrial activities in 2020 to stop the spread of COVID-19, haze still formed in Zhengzhou. We compared ionic compositions of PM2.5 during the period of the restriction with that immediately before the restriction and in the comparison period in 2019 to investigate the processes that caused the haze. The average concentration of PM2.5 was 83.9 µg m-3 in the restriction period, 241.8 µg m-3 before the restriction, and 94.0 µg m-3 in 2019. Nitrate was the largest contributor to the PM2.5 in all periods, with an average mass fraction of 24%-30%. The average molar concentration of total nitrogen compounds (NOx + nitrate) was 0.89 µmol m-3 in the restriction period, which was much lower than that in the non-restriction periods (1.85-2.74 µmol m-3). In contrast, the concentration of sulfur compounds (SO2 + sulfate) was 0.34-0.39 µmol m-3 in all periods. The conversion rate of NOx to nitrate (NOR) was 0.35 in the restriction period, significantly higher than that before the restriction (0.26) and in 2019 (0.25). NOR was higher with relative humidity in 40-80% in the restriction period than in the other two periods, whereas the conversion rate of SO2 to sulfate did not, indicating nitrate formation was more efficient during the restriction. When O3 occupied more than half of the oxidants (Ox = O3 + NO2), NOR increased rapidly with the ratio of O3 to Ox and was much higher in the daytime than nighttime. Therefore, haze in the restriction period was caused by increased NOx-to-nitrate conversion driven by photochemical reactions.

Direct observations of CO2 emission reductions due to COVID-19 lockdown across European urban districts.

The measures taken to contain the spread of COVID-19 in 2020 included restrictions of people's mobility and reductions in economic activities. These drastic changes in daily life, enforced through national lockdowns, led to abrupt reductions of anthropogenic CO2 emissions in urbanized areas all over the world. To examine the effect of social restrictions on local emissions of CO2, we analysed district level CO2 fluxes measured by the eddy-covariance technique from 13 stations in 11 European cities. The data span several years before the pandemic until October 2020 (six months after the pandemic began in Europe). All sites showed a reduction in CO2 emissions during the national lockdowns. The magnitude of these reductions varies in time and space, from city to city as well as between different areas of the same city. We found that, during the first lockdowns, urban CO2 emissions were cut with respect to the same period in previous years by 5% to 87% across the analysed districts, mainly as a result of limitations on mobility. However, as the restrictions were lifted in the following months, emissions quickly rebounded to their pre-COVID levels in the majority of sites.

Reduction in anthropogenic emissions suppressed new particle formation and growth: Insights from the COVID-19 lockdown

To control the spread of the novel 2019 Coronavirus (COVID-19), the Government of India enforced nationwide social and transportation restrictions (lockdown) in three phases from the evening of 24 March to 31 May 2020, which resulted in a significant reduction of primary emissions. Here, we performed the analyses of particle number size distribution measurements in the particle size range of 1.2-3 nm and 10-514 nm carried out from 15 April to 31 May 2020 (lockdown, LCD) and compared with measurements from the previous year during the same time period (15 April to 31 May 2019, business-as-usual, BAU) at University of Hyderabad in Hyderabad, India. The number concentrations of sub-3nm particles were comparable between LCD and BAU, but the number concentrations of particles greater than 10 nm diameter were lower by about 85% during LCD than BAU. It indicates that the reduction in primary anthropogenic emissions did not inhibit the formation of sub-3nm particles. But the frequency of occurrence of the new particle formation and growth (NPF&G) events was three-fold lower during LCD than BAU. The ratio of formaldehyde to nitrogen dioxide indicated that India falls in a NOx-limited regime, which reduces ambient ozone concentrations (lower condensable vapors via ozone oxidation of volatile organic compounds). Besides, the lower temperature (lower hydroxyl radical concentration) and lower wind speed during LCD may have contributed to the suppression of NPF&G events. Therefore, we emphasize the need to account for processes and interactions related to NPF&G in formulating particulate pollution mitigation policies in urban environments. This article is protected by copyright. All rights reserved.

Gradient Boosting Machine to Assess the Public Protest Impact on Urban Air Quality

Political and economic protests build-up due to the financial uncertainty and inequality spreading throughout the world. In 2019, Latin America took the main stage in a wave of protests. While the social side of protests is widely explored, the focus of this study is the evolution of gaseous urban air pollutants during and after one of these events. Changes in concentrations of NO2, CO, O3 and SO2 during and after the strike, were studied in Quito, Ecuador using two approaches: (i) inter-period observational analysis;and (ii) machine learning (ML) gradient boosting machine (GBM) developed business-as-usual (BAU) comparison to the observations. During the strike, both methods showed a large reduction in the concentrations of NO2 (31.5–32.36%) and CO (15.55–19.85%) and a slight reduction for O3 and SO2. The GBM approach showed an exclusive potential, especially for a lengthier period of predictions, to estimate strike impact on air quality even after the strike was over. This advocates for the use of machine learning techniques to estimate an extended effect of changes in human activities on urban gaseous pollution.