Effects of COVID-19 pandemic control measures on air pollution in Lima metropolitan area, Peru in South America

An exploratory analysis of PM 2.5 /PM 10 ratio during spring 2016-2018 in Metropolitan Lima

Aerosols (PM2.5 and PM10 ) represent one of the most critical pollutants due to their negative effects on human health. This research analyzed the relationship of PM and its PM2.5 /PM10 ratios with climatic variables in the austral spring (2016-2018) in Metropolitan Lima. Overall, there was an average PM2.5 /PM10 ratio of 0.33 with fluctuations from 0.30 to 0.35. However, there have also been high point values that reached ratios greater than one. This situation indicates a moderate condition of contamination by particulate matter with a predominance of coarse aerosols in spring, with an increasing trend over the years. The locations Ate and Villa Maria del Triunfo, especially Ate, presented poor quality conditions. Thursdays showed outstanding pollution peaks by PM10 , and a decrease is visible on Sundays. On the other hand, the PM2.5 showed a similar pattern every day, including Sundays. The maximum peaks occurred in the morning and night hours. The increase in anthropogenic emissions associated with the formation of secondary aerosols has been evident, being the case of the location Campo de Marte, the one that had a significant increase in ratios PM2.5 /PM10 , which confirms a greater intensity of secondary formations of carbonaceous particles from industrial oil sources, vehicle exhaust, as well as aerosols from metal smelting and biomass burning. There were negative correlations of the ratios with PM10 , temperature, wind speed, and direction, and positive correlations with PM2.5 and relative humidity. Contour lines were successfully developed that demonstrated the interaction of climate with PM2.5 /PM10 ratios. This will deepen the exploration of emission sources and modeling, which allows for optimizing air quality indices to control emissions and adequately manage air quality in Metropolitan Lima.

Reduction of the concentration of particulate material at a sampling point in Cusco city at the beginning of the pandemic

The pandemic produced by SARS-CoV-2 generated various impacts on public health, the environment and other anthropogenic activities. The purpose of this study was to evaluate the reduction of air pollution due to [Formula: see text] and [Formula: see text] particulate matter in Cusco city at the beginning of the pandemic. Social confinement in Peru began on March 16, 2020, until the end of June. These health measures caused strict isolation that resulted in a significant decrease in vehicle flow on the streets and avenues of the city of Cusco. In the first days of May, even during the time of confinement, we managed to measure air quality at a sampling point located on the campus of the Universidad Nacional de San Antonio Abad de Cusco; a reduction in air pollution due to particulate matter was observed. The evaluation was carried out using an high-volume (HiVol) 3000 particulate matter sampler and the mass of particulate matter adhered to the filters was determined by gravimetry. The concentrations of particulate matter [Formula: see text] and [Formula: see text] obtained pre-pandemic were compared with those recorded at the beginning of the pandemic. The results revealed a significant average reduction in the concentration of [Formula: see text] and [Formula: see text], reaching - 57.43% and - 59.52%, respectively, compared to pre-pandemic values. At the same time, its relationship with meteorological parameters and Google mobility data was evaluated and it was concluded that these parameters did not significantly affect the reduction of particulate matter. This study reveals the positive effects of the pandemic in reducing air pollution and the confinement measures had as a secondary effect on the decrease in air pollution in Cusco City.

Impact of COVID-19 restrictions on hourly levels of PM10, PM2.5 and black carbon at an industrial suburban site in northern Spain

Due to the COVID-19 pandemic, lockdown restrictions were established around the world. Many studies have assessed whether these restrictions affected atmospheric pollution. Comparison between them is difficult as the periods of time considered are generally not the same and thus, different conclusions may be reached. Besides, most of them consider mean daily pollutant concentration, despite differences being observed according to the time of day. In this study, the hourly levels of PM10, PM2.5 and black carbon (BC) in an industrial suburban area in the north of Spain were analysed from May 2019 to June 2020 and compared with those from the literature, using the same period in each case. In general, the highest concentrations were reached when the wind direction came from the southwest (where a steelworks, a coal-fired power plant and other industries are located) and during the night-time, both before and during the lockdown. The highest concentrations of PM10, PM2.5 and BC were observed from December to February (on average: 45, 17 and 1.3 μg m^-3, respectively). The decrease/increase in those pollutants levels during the lockdown were found to be highly dependent on the period considered. Indeed, PM10 can be found to decrease by up to 39% or increase by 12%; PM2.5 can decrease by 21% or increase by up to 36%; and BC, although it generally decreases (by up to 42%), can increase by 7.4%.

Impact of mobility restrictions on NO2 concentrations in key Latin American cities during the first wave of the COVID-19 pandemic

Between March and June 2020, activity in the major cities of Latin America declined due to containment efforts implemented by local governments to avoid the rapid spread of COVID-19. Our study compared 2020 with the previous year and demonstrated a considerable drop in tropospheric NO₂ levels obtained by the SENTINEL 5P satellite in major Latin American cities. Lima (47.5%), Santiago (36.1%), São Paulo (27%), Rio de Janeiro (23%), Quito (18.6%), Bogota (17.5%), Buenos Aires (16.6%), Guayaquil (15.3%), Medellin (14.2%), La Paz (9.5%), Belo Horizonte (7.8%), Mexico (7.6%) and Brasilia (5.9%) registered statistically significant decreases in NO₂ concentrations during the study period. In addition, we analyzed mobility data from Google and Apple reports as well as meteorological information from atmospheric reanalysis data along with satellite fields between 2011 and 2020, and performed a refined multivariate analysis (non-negative matrix approximation) to show that this decrease was associated with a reduction in population mobility rather than meteorological factors. Our findings corroborate the argument that confinement scenarios may indicate how air pollutant concentrations can be effectively reduced and managed.

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.

Statistical modeling approach for PM10 prediction before and during confinement by COVID-19 in South Lima, Perú

A total of 188,859 meteorological-PM$$_{10}$$ 10 data validated before (2019) and during the COVID-19 pandemic (2020) were used. In order to predict PM$$_{10}$$ 10 in two districts of South Lima in Peru, hourly, daily, monthly and seasonal variations of the data were analyzed. Principal Component Analysis (PCA) and linear/nonlinear modeling were applied. The results showed the highest annual average PM$$_{10}$$ 10 for San Juan de Miraflores (SJM) (PM$$_{10}$$ 10 -SJM: 78.7 $$\upmu$$ μ g/m$$^{3}$$ 3 ) and the lowest in Santiago de Surco (SS) (PM$$_{10}$$ 10 -SS: 40.2 $$\upmu$$ μ g/m$$^{3}$$ 3 ). The PCA showed the influence of relative humidity (RH)-atmospheric pressure (AP)-temperature (T)/dew point (DP)-wind speed (WS)-wind direction (WD) combinations. Cool months with higher humidity and atmospheric instability decreased PM$$_{10}$$ 10 values in SJM and warm months increased it, favored by thermal inversion (TI). Dust resuspension, vehicular transport and stationary sources contributed more PM$$_{10}$$ 10 at peak times in the morning and evening. The Multiple linear regression (MLR) showed the best correlation (r = 0.6166), followed by the three-dimensional model LogAP-LogWD-LogPM$$_{10}$$ 10 (r = 0.5753); the RMSE-MLR (12.92) exceeded that found in the 3D models (RMSE $$<0.3$$ < 0.3 ) and the NSE-MLR criterion (0.3804) was acceptable. PM$$_{10}$$ 10 prediction was modeled using the algorithmic approach in any scenario to optimize urban management decisions in times of pandemic.

Immission levels and identification of sulfur dioxide sources in La Oroya city, Peruvian Andes

La Oroya is a city in the Peruvian Andes that has suffered a serious deterioration in its air quality, especially due to the high rate of sulfur dioxide (SO2) emissions, which underlines the importance of knowing its sources of contamination and variation over the years. In this sense, this study aimed to evaluate the immission levels and determine the sources of SO2 contamination in La Oroya. This analysis was performed using the hourly concentration data of SO2, and meteorological variables (wind speed and direction), which were analyzed for a period of three years (2018-2020). Graphs of time series, wind and pollutant roses, bivariate polar graphs, clustering k-means, nonparametric statistical tests, and the application of the conditional bivariate probability function were performed to analyze the data and identify the emission sources. The mean concentration of SO2 was 264.2 μg m-3 for the study period, where 55.66 and 2.37% of the evaluated days exceeded the guideline values recommended by the World Health Organization and the Peruvian Environmental Quality Standard for air for 24 h, respectively. The results showed a defined pattern for the daily and monthly variations, with peaks in the morning hours (0900-1000 h LT) and at the end of the year (December), respectively. The main sources of SO2 emissions identified were light and heavy vehicles that travel through the Central Highway, the La Oroya Metallurgical Complex, the transit of vehicles within the city, and the diesel-electric locomotives that provide cargo transportation services and tourism passenger transportation. The article attempts to contribute to the development of adequate air quality management policies.

Long-Term Impacts of COVID-19 Lockdown on the NO2 Concentrations and Urban Thermal Environment: Evidence from the Five Largest Urban Agglomerations in China

Under the threat of COVID-19, many regions around the world implemented lockdown policies to control the spread of the virus. This restriction on both social and economic activities has improved the quality of the environment in certain aspects. However, most previous studies have only focused on the short-term impact of lockdown policies on the urban environment. The long-term effects of lockdown require a more focused exploration and analysis. Thus, five major urban agglomerations in China were selected as the research area; changes in the numerical and spatial distribution of NO2 concentration and surface temperature during four different lockdown stages in 2019, 2020, and 2021 were investigated to analyze the long-term effects of lockdown policies on the urban environment. The results indicated that the impact of shorter lockdowns was short-term and unsustainable; the NO2 concentrations increased again with the resumption of production. Compared with air pollutants, thermal environmental problems are more complex. The effect of the lockdown policy was not reflected in the decrease in the area proportions of the high- and sub-high-temperature regions but rather in the spatial distribution of the high-temperature area, which was manifested as a fragmentation and dispersion of heat source patches. In addition to the severity of the lockdown, the impact of the lockdown policy was also closely related to the level of development and industrial structure of each city. Among the urban environments of the five agglomerations, the most affected were the Yangtze River Delta and Yangtze River Middle-Reach urban agglomerations, which had the largest decline in NO2 concentrations and the most notable fragmentation of heat source patches.

Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing

Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO₂ and AOD, while SO₂ unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO₂ and SO₂ were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO₂, respectively, while SO₂ showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.

Coupled multifractal methods to reveal changes in nitrogen dioxide and tropospheric ozone concentrations during the COVID-19 lockdown

Due to COVID-19 pandemic, the lockdown effects on air pollution level are undeniable. Several studies around the world have detected an uneven behaviour in tropospheric ozone (O ₃) concentrations. In this work, Seville (Spain) is used as example of faced to traffic place in which the nitrogen dioxide (NO ₂) is drastically reduced (41%) while O ₃ has no significant changes. In order to evaluate the existence of differences in O ₃ behaviour that is not detected by statistical procedures, a multifractal approach was used to assess the coupled scale relationship between NO ₂ and O ₃ during the 2020 lockdown against a period reference (2017-2019). For this purpose, the two main coupled multifractal method were employed: multifractal detrended cross-correlation and joint multifractal analysis. While cross-correlation analysis did not detect differences between the cross-correlated fluctuations of NO ₂ and O ₃ in the periods analysed, the joint multifractal analysis, based on the partition function and the method of moments, found a loss of variability in O ₃ during the lockdown. This leads to a loss of multifractal characteristic of O ₃ time series. The drastically reduction of primary pollutants during the lockdown might be the responsible of the tendency to monofractality in O ₃ time series. These differences were found for a wide temporal extent ranging from 80 min to ~28 days.

Air pollution and COVID-19 lockdown in a large South American city: Santiago Metropolitan Area, Chile

The implementation of confinement and physical distancing measures to restrict people's activities and transit in the midst of the COVID-19 pandemic allowed us to study how these measures affect the air quality in urban areas with high pollution rates, such as Santiago, Chile. A comparative study between the concentrations of PM10, PM2.5, NOx, CO, and O3 during the months of March to May 2020 and the corresponding concentrations during the same period in 2017-2019 is presented. A combination of surface measurements from the air quality monitoring network of the city, remote satellite measurements, and simulations of traffic activity and road transport emissions allowed us to quantify the change in the average concentrations of each pollutant. Average relative changes of traffic emissions (between 61% and 68%) implied statistically significant concentrations reductions of 54%, 13%, and 11% for NOx, CO, and PM2.5, respectively, during the pandemic period compared to historical period. In contrast, the average concentration of O3 increased by 63% during 2020 compared to 2017-2019. The nonlinear response observed in the pollution levels can be attributed to the changes in the vehicular emission patterns during the pandemic and to the role of other sources such as residential emissions or secondary PM.