Air quality and urban sustainable development: the application of machine learning tools

[EN] Air quality has an efect on a population¿s quality of life. As a dimension of sustainable urban development, governments have been concerned about this indicator. This is refected in the references consulted that have demonstrated progress in forecasting pollution events to issue early warnings using conventional tools which, as a result of the new era of big data, are becoming obsolete. There are a limited number of studies with applications of machine learning tools to characterize and forecast behavior of the environmental, social and economic dimensions of sustainable development as they pertain to air quality. This article presents an analysis of studies that developed machine learning models to forecast sustainable development and air quality. Additionally, this paper sets out to present research that studied the relationship between air quality and urban sustainable development to identify the reliability and possible applications in diferent urban contexts of these machine learning tools. To that end, a systematic review was carried out, revealing that machine learning tools have been primarily used for clustering and classifying variables and indicators according to the problem analyzed, while tools such as artifcial neural networks and support vector machines are the most widely used to predict diferent types of events. The nonlinear nature and synergy of the dimensions of sustainable development are of great interest for the application of machine learning tools.Molina-Gómez, NI.; Díaz-Arévalo, JL.; López Jiménez, PA. (2021). Air quality and urban sustainable development: the application of machine learning tools. 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A global observational analysis to understand changes in air quality during exceptionally low anthropogenic emission

This global study, which has been coordinated by the World Meteorological Organization Global Atmospheric Watch (WMO/GAW) programme, aims to understand the behaviour of key air pollutant species during the COVID-19 pandemic period of exceptionally low emissions across the globe. We investigated the effects of the differences in both emissions and regional and local meteorology in 2020 compared with the period 2015–2019. By adopting a globally consistent approach, this comprehensive observational analysis focuses on changes in air quality in and around cities across the globe for the following air pollutants PM2.5, PM10, PMC (coarse fraction of PM), NO2, SO2, NOx, CO, O3 and the total gaseous oxidant (OX = NO2 + O3) during the pre-lockdown, partial lockdown, full lockdown and two relaxation periods spanning from January to September 2020. The analysis is based on in situ ground-based air quality observations at over 540 traffic, background and rural stations, from 63 cities and covering 25 countries over seven geographical regions of the world. Anomalies in the air pollutant concentrations (increases or decreases during 2020 periods compared to equivalent 2015–2019 periods) were calculated and the possible effects of meteorological conditions were analysed by computing anomalies from ERA5 reanalyses and local observations for these periods. We observed a positive correlation between the reductions in NO2 and NOx concentrations and peoples’ mobility for most cities. A correlation between PMC and mobility changes was also seen for some Asian and South American cities. A clear signal was not observed for other pollutants, suggesting that sources besides vehicular emissions also substantially contributed to the change in air quality. As a global and regional overview of the changes in ambient concentrations of key air quality species, we observed decreases of up to about 70% in mean NO2 and between 30% and 40% in mean PM2.5 concentrations over 2020 full lockdown compared to the same period in 2015–2019. However, PM2.5 exhibited complex signals, even within the same region, with increases in some Spanish cities, attributed mainly to the long-range transport of African dust and/or biomass burning (corroborated with the analysis of NO2/CO ratio). Some Chinese cities showed similar increases in PM2.5 during the lockdown periods, but in this case, it was likely due to secondary PM formation. Changes in O3 concentrations were highly heterogeneous, with no overall change or small increases (as in the case of Europe), and positive anomalies of 25% and 30% in East Asia and South America, respectively, with Colombia showing the largest positive anomaly of ~70%. The SO2 anomalies were negative for 2020 compared to 2015–2019 (between ~25 to 60%) for all regions. For CO, negative anomalies were observed for all regions with the largest decrease for South America of up to ~40%. The NO2/CO ratio indicated that specific sites (such as those in Spanish cities) were affected by biomass burning plumes, which outweighed the NO2 decrease due to the general reduction in mobility (ratio of ~60%). Analysis of the total oxidant (OX = NO2 + O3) showed that primary NO2 emissions at urban locations were greater than the O3 production, whereas at background sites, OX was mostly driven by the regional contributions rather than local NO2 and O3 concentrations. The present study clearly highlights the importance of meteorology and episodic contributions (e.g., from dust, domestic, agricultural biomass burning and crop fertilizing) when analysing air quality in and around cities even during large emissions reductions. There is still the need to better understand how the chemical responses of secondary pollutants to emission change under complex meteorological conditions, along with climate change and socio-economic drivers may affect future air quality. The implications for regional and global policies are also significant, as our study clearly indicates that PM2.5 concentrations would not likely meet the World Health Organization guidelines in many parts of the world, despite the drastic reductions in mobility. Consequently, revisions of air quality regulation (e.g., the Gothenburg Protocol) with more ambitious targets that are specific to the different regions of the world may well be required.Peer reviewedFinal Published versio

Air Quality in Quito during COVID-19 outbreak

In early 2020, the human population of the world confronted a great challenge – a pandemic of a novel Corona Virus disease (COVID-19). In order to protect the population, precautionary quarantine measures were implemented in almost all countries by March 2020, which focuses on social isolation and distancing through a strict limitation of anthropogenic activities: traffic, social gatherings, industries, etc. In turn, this epidemiologic emergency has resulted in reduced environmental pollution. It is necessary to quantify the effect of these measures on the urban air pollution in the Ecuadorian capital - Quito and its spatial variations, as toxic pollution tends to aggravate the body’s response to the respiratory diseases, including COVID-19. The data "QuitoDATA_2020.xlsx" contains atmospheric information for Quito starting January 1 and ending April 12, 2020, englobing a few months before the quarantine and four weeks of quarantine. This data is acquired from the urban air quality monitoring network run by the local Secretariat of the Environment. This high-quality monitoring data network is run strictly following the recommendations of the Environmental Protection Agency (EPA) using standard EPA methods (http://www.quitoambiente.gob.ec/ambiente/). Daily average data is organized per study site (Belisario, Carapungo, Centro, Cotocollao, Camal, Guamani and Chillos) and also is averaged over the city (Quito)). The data confirms a great reduction (29-68%) in major urban pollutants (nitrogen dioxide - NO2, sulfur dioxide - SO2, particulate matter with aerodynamic diameters ≤ 2.5 µm - PM2.5 and carbon monoxide - CO). Important meteorological parameters (hourly precipitation (Rain) and wind speed (WS)) are also presented in order to help support the discussion. These data show that the reduction in air pollution levels is not due to extremely different environmental conditions, but actually due to the quarantine measures. The spatial evolution of atmospheric pollution is also studied for Quito and Ecuador, using observed surface and satellite data. This data is presented in “data_NO2_SO2_S5P.xlsx” and S5P data “NO2 DATA S5P.rar” and “SO2 DATA S5P.rar”. The Sentinel 5 Precursor TROPOMI satellite, is the most advanced equipment on earth, is provided with spectral sensors capable of detecting concentrations of polluting gases such as NO2, SO2, O3, CO, CH4, Formaldehydes and Aerosols in profile, with a daily temporality; data are available from 2018 and are granted by the Royal Netherlands Meteorological Research Institute (KNMI). For this research we used the Google Engine (GGE) platform which allows downloading L3 level products, GGE uses HARP commands which gives us images with 1km per pixel resolution.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Assessing the COVID-19 impact on air quality in Quito, Ecuador

The COVID-19 lockdown impact on air quality is estimated for Andean city - Quito. This data set includes hourly data for a number of sites (urban: Bel, Car, Cam; and suburban: Cot, Gua, Chi) in the Ecuadorian capital for atmospheric and meteorological parameters. The changes in pollution are quantified, showing that during the full lockdown (16 March - 1 June, 2020), air pollution decreased by -53%, -45%, -30%, and -15% for NO2, SO2, CO, and PM2.5, respectively. The traffic-busy districts (Bel and Cot) were the most affected. However, the air pollution starts increasing during the later stages of the full lockdown. Even more, during the partial relaxation (starting on 2 June, 2020), the concentrations nearly return to the pre-pandemic levels

Chemically-resolved aerosol eddy covariance flux measurements in urban Mexico City during MILAGRO 2006

As part of the MILAGRO 2006 field campaign, the exchange of atmospheric aerosols with the urban landscape was measured from a tall tower erected in a heavily populated neighborhood of Mexico City. Urban submicron aerosol fluxes were measured using an eddy covariance method with a quadrupole aerosol mass spectrometer during a two week period in March, 2006. Nitrate and ammonium aerosol concentrations were elevated at this location near the city center compared to measurements at other urban sites. Significant downward fluxes of nitrate aerosol, averaging −0.2 μg m−2 s−1, were measured during daytime. The urban surface was not a significant source of sulfate aerosols. The measurements also showed that primary organic aerosol fluxes, approximated by hydrocarbon-like organic aerosols (HOA), displayed diurnal patterns similar to CO2 fluxes and anthropogenic urban activities. Overall, 47% of submicron organic aerosol emissions were HOA, 35% were oxygenated (OOA) and 18% were associated with biomass burning (BBOA). Organic aerosol fluxes were bi-directional, but on average HOA fluxes were 0.1 μg m−2 s−1, OOA fluxes were −0.03 μg m−2 s−1, and BBOA fluxes were −0.03 μg m−2 s−1. After accounting for size differences (PM1 vs PM2.5) and using an estimate of the black carbon component, comparison of the flux measurements with the 2006 gridded emissions inventory of Mexico City, showed that the daily-averaged total PM emission rates were essentially identical for the emission inventory and the flux measurements. However, the emission inventory included dust and metal particulate contributions, which were not included in the flux measurements. As a result, it appears that the inventory underestimates overall PM emissions for this location