Nationwide CoViD-19 lockdown impact on air quality in India

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

Is Exposure to PM2.5 and PM10, a Factor of Surge of 2nd Wave of COVID-19- A Case Study of Delhi, India?

The full text of this preprint has been withdrawn by the authors while they make corrections to the work. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

A Bayesian parametric approach to the retrieval of the atmospheric number size distribution from lidar data

We consider the problem of reconstructing the number size distribution (or particle size distribution) in the atmosphere from lidar measurements of the extinction and backscattering coefficients. We assume that the number size distribution can be modeled as a superposition of log-normal distributions, each one defined by three parameters: mode, width and height. We use a Bayesian model and a Monte Carlo algorithm to estimate these parameters. We test the developed method on synthetic data generated by distributions containing one or two modes and perturbed by Gaussian noise as well as on three datasets obtained from AERONET. We show that the proposed algorithm provides good results when the right number of modes is selected. In general, an overestimate of the number of modes provides better results than an underestimate. In all cases, the PM1, PM2.5 and PM10 concentrations are reconstructed with tolerable deviations.

Effect of Air Purification Systems on Particulate Matter and Airborne Bacteria in Public Buses

The indoor air quality in public transport systems is a major concern in South Korea. Within this context, we investigated the effect of air purification systems on the indoor air quality of intercity buses, one of the most popular transport options in South Korea. Air purifiers were custom designed and equipped with high-efficiency particulate air (HEPA) filters to remove particulate matter and ultraviolet light-emitting diodes (UV-LEDs) to remove airborne bacteria. To investigate the effectiveness of the air purification systems, we compared concentrations of particulate matter (PM2.5 and PM10), airborne bacteria, and carbon dioxide (CO2) in six buses (three with air purification systems and three without) along three bus routes (BUS1, BUS2, BUS3) in Gyeonggi Province, South Korea, between 6 April and 4 May 2021. Compared to the buses without air purification, those with air purification systems showed 34–60% and 25–61% lower average concentrations of PM2.5 and PM10, respectively. In addition, buses with air purification systems had 24–78% lower average airborne bacteria concentrations compared to those without air purification systems (when measured after 30 min of initial purification).

Spatial-Temporal Variations and Socio-Economic Influencing Factors of Air Quality in China’s Major Cities During COVID-19

Due to the emergence of COVID-19 in Wuhan in January 2020, the central government of China announced that Wuhan was in "lockdown," the activities of the country's citizens were restricted. This study selected three standard air quality indexes AQI, PM2.5, and PM10 of 2017-2021 in 40 major cities of seven regions in China to analyze their changes, spatial-temporal distributions, and socio-economic influencing factors. Compared with 2019, AQI, PM2.5, and PM10 decreased by 22.54%, 13.94%, and 22.30%, respectively, and the days with AQI level "A" increased from 89% to 100% during the "lockdown" in 2020. Due to different degrees of industrialization, the decline range of Northeast, Yangtze River Delta, and Pearl River Delta areas is more than that of the Southwest, BTH, Northwest, and Central areas, the concentration of air pollutants shows significant regional characteristics. The AQI before and after the "lockdown" in 2020 showed significant spatial autocorrelation, and the cities' AQI in the north present high aggregation, and the cities in the south are in low aggregation. From the data at the national level, the changes of the four socio-economic factors of roadway passenger volume (RPV), construction area (CA), coal-fired power (CP), and the proportion of industrial added value in GDP (IND) significantly influenced AQI. This study gives regulators confidence that if the government implements regionalized air quality improvement policies according to the characteristics of each region in China and reasonably plans socio-economic activities, it is expected to improve China's air quality sustainably.

A Study of PM 10, PM 2.5 Concentrations in the Atmospheric Air in Kraków, Poland

The air in Kraków is one of the most polluted in Europe. Polish standards for notification and alert levels for PM10 particulate matterare one of the the highest in Europe and exceed the recommendations of the World Health Organization (WHO) for safe daily concentrations by several times. The article presents the results of airborne dust measurements in three districts of Kraków. The study hasshown that the concentration of PM2.5 and PM10 particulate matter exceeded the annual average permissible levels. Empirical measurements of PM2.5 show significantly higher air pollution values than the data notified by stationary monitoring stations installed intwo locations. The high value of Pearson linear correlation coefficient confirms that weather conditions have a significant impact on airquality in Kraków. Wind speed in the autumn and winter seasons has by far the greatest influence on air quality in al. Krasińskiego,in the Ruczaj and Kurdwanów districts. A strong negative correlation was displayed. Manual measurements should be used to verifydata obtained from air monitoring stations. It is to be expected that, in Kraków, air purity will improve due to the implementation ofan anti-smog resolution and subsidies for the replacement of obsolete heating systems with more environmentally friendly solutions.

A Sensor-Based Drone for Pollutants Detection in Eco-Friendly Cities: Hardware Design and Data Analysis Application

The increase in produced waste is a symptom of inefficient resources usage, which should be better exploited as a resource for energy and materials. The air pollution generated by waste causes impacts felt by a large part of the population living in and around the main urban areas. This paper presents a mobile sensor node for monitoring air and noise pollution; indeed, the developed system is installed on an RC drone, quickly monitoring large areas. It relies on a Raspberry Pi Zero W board and a wide set of sensors (i.e., NO2, CO, NH3, CO2, VOCs, PM2.5, and PM10) to sample the environmental parameter at regular time intervals. A proper classification algorithm was developed to quantify the traffic level from the noise level (NL) acquired by the onboard microphone. Additionally, the drone is equipped with a camera and implements a visual recognition algorithm (Fast R-CNN) to detect waste fires and mark them by a GPS receiver. Furthermore, the firmware for managing the sensing unit operation was developed, as well as the power supply section. In particular, the node’s consumption was analysed in two use cases, and the battery capacity needed to power the designed device was sized. The onfield tests demonstrated the proper operation of the developed monitoring system. Finally, a cloud application was developed to remotely monitor the information acquired by the sensor-based drone and upload them on a remote database.

PM₁₀ variation, composition, and source analysis in Tuscany (Italy) following the COVID-19 lockdown restrictions

To control the spread of COVID-19, in March 2020 exceptional restrictive measures were taken imposing a radical change in the lifestyle of millions of citizens around the world, albeit for a short period of time. The national lockdown, which in Italy lasted from 10 March to 18 May 2020, was a unique opportunity to observe the variation in air quality in urban environments in a condition of almost total traffic block and a strong reduction in work activities. In this paper, the data from seventeen urban monitoring sites in Tuscany are presented by comparing PM and NO2 of the two months before the start of the lockdown and the two after with the corresponding months of the previous three years. The results show that the total load of PM2.5 and PM10 decreased but it did not exhibit significant changes compared to previous years, while NO2 undergoes a drastic reduction. For three of these sites, the chemical composition of the collected samples was measured by thermo-optical, ion chromatography and PIXE analysis, and the application of multivariate PMF analysis allowed the PM10 source identification and apportionment. Thanks to these analyses it was possible to explain the low sensitiveness of PM10 to the lockdown effects as due to different, sometimes opposite, behaviors of the different sources that contribute to PM. The results clearly indicated a decline in pollution levels related to urban traffic and an increase in the concentration of sulfate for all sites during the lockdown period.

Dynamic of Particulate Matter for Quotidian Aerosol Sources in Indoor Air

A correlation between the mass concentration of particulate matter (PM) and the occurrence of health-related problems or diseases has been confirmed by several studies. However, little is known about indoor PM concentrations, their associated risks or their impact on health. In this work, the PM1, PM2.5 and PM10 produced by different indoor aerosol sources (candles, cooking, electronic cigarettes, tobacco cigarettes, mosquito coils and incense) are studied. The purpose is to quantify the emission characteristics of different indoor particle sources. The mass concentration, the numerical concentration, and the size distribution of PM from various sources were determined in an examination room 65 m3 in volume. Sub-micrometer particles and approximations of PM1, PM2.5 and PM10 concentrations were measured simultaneously using a diffusion aerosol spectrometer (DAS). The ultrafine particle concentration for the studied indoor aerosol sources was approximately 7 × 104 particles/cm3 (incense, mosquito coils and electronic cigarettes), 1.2 × 105 particles/cm3 for candles and cooking and 2.7 × 105 particles/cm3 for tobacco cigarettes. The results indicate that electronic cigarettes can raise indoor PM2.5 levels more than 100 times. PM1 concentrations can be nearly 55 and 30 times higher than the background level during electronic cigarette usage and tobacco cigarette burning, respectively. It is necessary to study the evaluation of indoor PM, assess the toxic potential of internal molecules and develop and test strategies to ensure the improvement of indoor air quality.