Predicting the Impact of Change in Air Quality Patterns Due to COVID-19 Lockdown Policies in Multiple Urban Cities of Henan: A Deep Learning Approach

Several countries implemented prevention and control measures in response to the 2019 new coronavirus virus (COVID-19) pandemic. To study the impact of the lockdown due to COVID-19 on multiple cities, this study utilized data from 18 cities of Henan to understand the air quality pattern change during COVID-19 from 2019 to 2021. It examined the temporal and spatial distribution impact. This study firstly utilized a deep learning bi-directional long-term short-term (Bi-LSTM) model to predict air quality patterns during 3 periods, i.e., COVID-A (before COVID-19, i.e., 2019), COVID-B (during COVID-19, i.e., 2020), COVID-C (after COVID-19 cases, i.e., 2021) and obtained the R-2 value of more than 72% average in each year and decreased MAE value, which was better than other studies' deep learning methods. This study secondly focused on the change of pollutants and observed an increase in Air Quality Index by 10%, a decrease in PM2.5 by 14%, PM10 by 18%, NO2 by 14%, and SO2 by 16% during the COVID-B period. This study found an increase in O-3 by 31% during the COVID-C period and observed a significant decrease in pollutants during the COVID-C period (PM10 by 42%, PM2.5 by 97%, NO2 by 89%, SO2 by 36%, CO by 58%, O-3 by 31%). Lastly, the impact of lockdown policies was studied during the COVID-B period and the results showed that Henan achieved the Grade I standards of air quality standards after lockdown was implemented. Although there were many severe effects of the COVID-19 pandemic on human health and the global economy, lockdowns likely resulted in significant short-term health advantages owing to reduced air pollution and significantly improved ambient air quality. Following COVID-19, the government must take action to address the environmental problems that contributed to the deteriorating air quality.

Impacts of travel bans and travel intention changes on aviation emissions due to Covid-19 pandemic.

The Covid-19 pandemic negatively affected many sectors including aviation and travel. Travel bans and forced lockdowns prevented transportation activity, especially air travel. Accordingly, huge amounts of emission reductions occurred. On the other hand, travel restrictions are not the only cause of emissions reductions. Changing travel intention in the era of Covid-19 is another important factor that affects aviation emissions. This paper aims to investigate the Landing/Take-Off (LTO) emission changes at Turkish airports. An emission inventory has been implemented for the years 2019 and 2020 to reveal the impacts of Covid-19 on aviation emissions. Domestic, international, and cargo flights have been included in the inventory. According to the results, total emissions of SO2, CO2, CO, NOx, NMVOC, CH4, N2O, and PM2.5 have decreased in 2020 compared to 2019 by 49.8%, 49.7%, 41.0%, 52.6%, 40.0%, 33.8%, 49.8%, and 50.3%, respectively. Total CO2 reductions in the Q2, Q3, and Q4 periods of 2020 compared to that of 2019 are 87%, 50% and 43%, respectively. Another aim of this paper is to find the underlying reasons for emission reductions. For Turkish airports, emission reductions have resulted from travel bans in Q2. After the relaxation of restrictions with the declaration of the "New Normal" in Turkey, flight traffic rebounded to a certain level but was lower than 2019 levels. Therefore, changing travel intention is the main cause of emission reductions in Q3 and Q4 of 2020. The results of this study contribute to both the areas of air pollution and tourism management. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-023-02916-8.

[Evolution and Characteristics of Full-process Vehicular VOCs Emissions in Tianjin from 2000 to 2020].

Vehicle emissions are an important source of anthropogenic volatile organic compound (VOCs) emissions in urban areas and are commonly quantified using vehicle emission inventories. However, most previous studies on vehicle emission inventories have incomplete emission factors and emission processes or insufficient consideration of meteorological parameters. Based on the localized full-process emission factors attained from tested data and previous studies, a method to develop a monthly vehicular VOC emission inventory of full process for the long-term was established, which covered exhaust and evaporative emissions (including running loss, diurnal breathing loss, hot soak loss, and refueling emission). Then, the method was used to develop a full-process vehicular VOC emission inventory in Tianjin from 2000 to 2020. The results showed that the total vehicular VOC emissions in Tianjin rose slowly and then gradually decreased. In 2020, the total emissions were 21400 tons. The light-duty passenger vehicles were the dominant contributors and covered 75.00% of the total emissions. Unlike the continuous decline in exhaust emissions, evaporative emissions showed an inverted U-shaped trend with an increasing contribution to total emissions yearly, accounting for 31.69% in 2020. Monthly emissions were affected by both vehicle activity and emission factors. VOC emissions were high in autumn and winter and low in spring and summer. During the COVID-19 epidemic in 2020, vehicle activity was limited by closure and control, making VOC emissions significantly lower than those during the same period in previous years. The method and data in this study can provide technical reference and a decision-making basis for air pollution prevention and control.

Emission footprint of 2019 cruise traffic in the port of Dubrovnik

In this study, the environmental footprint within a designated area is determined by an inventory of air pollutants (including toxic and greenhouse gases) emissions generated. The designated area of concern is the port of Dubrovnik, a well-known cruise ship destination, where a major source of pollutant emissions is the diesel engines of the ships operating in the port. This research was undertaken for the port of Dubrovnik in connection with the development of the national strategy and the need for determining the inventory of air pollutants. It was conducted for the last pre – COVID-19 year, 2019. In this paper, after a short introduction, the basic data of the port of Dubrovnik and the marine traffic (predominantly cruisers) in 2019 are provided, obtained from publicly available data. Next, the emission estimate methodology based on a bottom-up approach is described. The inventory analysis was undertaken from the port boundary to the PWD (pier/wharf/dock) and back. The basic equations for evaluation during cruising, maneuvering, and hoteling are given along with the corresponding data. The aggregated results are presented in the form of tables and column charts. These results show that the generation of CO2 highly dominates. Regarding the pollution analyzed NOx dominates. The results of this study could be of interest for later studies on environmental pollution in the region of Dubrovnik-Neretva County and the Croatian coast. © Faculty of Maritime Studies Rijeka, 2022.

Accurate flight emission inventory estimation of Chinese civil aviation flights with QAR data

Rapid growth of emissions from Chinese civil aviation in recent years has largely accompanied with its fast expansion，and accurate emission inventory estimation is necessary for assessing the environmental impacts of the domestic civil aviation industry. To address the shortcomings of current studies in terms of accuracy and coverage， we proposed a bottom-up approach to estimate fuel consumptions and emissions， including HC，CO，NOx，PM，SO2，and CO2 from each individual flight with finer-scaled quick access recorder（QAR）data，collected from the domestic flights from January 2018 to November 2021. In this study，the durations of each flight phase were concerned with respect to various aircraft types，take-off and landing airports. Results show that the larger the airport is and the higher the emissions from this single flight is，and emissions during the CCD phase are the most important part and positively related to the flight duration. Emissions from 2018 to 2019 shew increasements，where the total emissions of CO2 were 73.17 ×106 t and 76.85 ×106 t，respectively. However，unexpected fluctuations occurred since the end of 2019 due to the outbreak of Covid19 epidemic，where the emission of CO2 were 61.51 ×106 t and 60.08 ×106 t in 2020 and 2021（January to November）. To explore the impacts of the Covid19 on flight emissions，an Autoregressive Integrated Moving Average（ARIMA）model was used to estimate emissions after December 2019 with assuming the absence of Covid19，and results show that total emissions decreased by about 20% up to November 2021. © 2022 Science Press. All rights reserved.

The Characteristics of Light-Duty Passenger Vehicle Mileage and Impact Analysis in China from a Big Data Perspective

Vehicle mileage is one of the key parameters for accurately evaluating vehicle emissions and energy consumption. With the support of the national annual vehicle emission inspection networked platform in China, this study used big data methods to analyze the activity level characteristics of the light-duty passenger vehicle fleet with the highest ownership proportion. We found that the annual mileage of vehicles does not decay significantly with the increase in vehicle age, and the mileage of vehicles is relatively low in the first few years due to the run-in period, among other reasons. This study indicated that the average mileage of the private passenger car fleet is 10,300 km/yr and that of the taxi fleet was 80,000 km/yr in China in 2019, and the annual mileage dropped by 22% in 2020 due to the pandemic. Based on the vehicle mileage characteristics, the emission inventory of major pollutants from light-duty passenger vehicles in China for 2010–2020 was able to be updated, which will provide important data support for more accurate environmental and climate benefit assessments in the future. [ FROM AUTHOR]

Four-dimensional variational assimilation for SO2emission and its application around the COVID-19 lockdown in the spring 2020 over China

Emission inventories are essential for modelling studies and pollution control, but traditional emission inventories are usually updated after a few years based on the statistics of "bottom-up"approach from the energy consumption in provinces, cities, and counties. The latest emission inventories of multi-resolution emission inventory in China (MEIC) was compiled from the statistics for the year 2016 (MEIC_2016). However, the real emissions have varied yearly, due to national pollution control policies and accidental special events, such as the coronavirus disease (COVID-19) pandemic. In this study, a four-dimensional variational assimilation (4DVAR) system based on the "top-down"approach was developed to optimise sulfur dioxide (SO2) emissions by assimilating the data of SO2 concentrations from surface observational stations. The 4DVAR system was then applied to obtain the SO2 emissions during the early period of COVID-19 pandemic (from 17 January to 7 February 2020), and the same period in 2019 over China. The results showed that the average MEIC_2016, 2019, and 2020 emissions were 42.2×106, 40.1×106, and 36.4×106 kg d-1. The emissions in 2020 decreased by 9.2 % in relation to the COVID-19 lockdown compared with those in 2019. For central China, where the lockdown measures were quite strict, the mean 2020 emission decreased by 21.0 % compared with 2019 emissions. Three forecast experiments were conducted using the emissions of MEIC_2016, 2019, and 2020 to demonstrate the effects of optimised emissions. The root mean square error (RMSE) in the experiments using 2019 and 2020 emissions decreased by 28.1 % and 50.7 %, and the correlation coefficient increased by 89.5 % and 205.9 % compared with the experiment using MEIC_2016. For central China, the average RMSE in the experiments with 2019 and 2020 emissions decreased by 48.8 % and 77.0 %, and the average correlation coefficient increased by 44.3 % and 238.7 %, compared with the experiment using MEIC_2016 emissions. The results demonstrated that the 4DVAR system effectively optimised emissions to describe the actual changes in SO2 emissions related to the COVID lockdown, and it can thus be used to improve the accuracy of forecasts. Copyright: © 2022 Yiwen Hu et al.

Evolution of India's PM2.5 pollution between 1998 and 2020 using global reanalysis fields coupled with satellite observations and fuel consumption patterns

General practice is to rely on ambient monitoring data for reporting and regulatory applications in air quality management. Based on data collated for 2021, Delhi ranked the most polluted capital city, and another 62 Indian cities are in the top 100 most polluted cities list (https://www.iqair.com). This path limits the scrutiny and evaluation only to the cities with a monitoring station, neglecting a large section of non-urban areas. In this paper, we present a summary of evolution of PM2.5 pollution in India between 1998 and 2020, using reanalysed ground-level PM2.5 concentrations estimated by combining satellite AOD retrievals with chemical transport model results from a GEOS-Chem-CEDS system, and subsequently calibrated to on-ground observations. Between 1998 and 2020, India's annual average PM2.5 values steadily increased across the country, Delhi remained the most polluted state in all the years, and total population complying with the annual ambient standard of 40 mu g m(-3) dropped from 60.5% to 28.4%. According to the GBD-MAPS program, 81% of PM2.5 pollution in India is sourced to fuel (coal, petrol, diesel, gas, biomass, and waste) combustion that supports daily activities in the fields of personal transport, freight transport, electricity generation, industrial manufacturing, cooking, heating, construction, road dust resuspension, and waste burning. We overlayed the pollution trend with fuel consumption and activity patterns to further explain this evolution. While the 2 month COVID-19 lockdown period in 2020 provides evidence to argue that the only way to achieve "clean air" is by (a) cutting emissions at all the sources and (b) cutting emissions regionally, some hard decisions are required to enable and sustain larger reductions across sectors to reach not only the national ambient standard, but also the WHO guideline of 5 mu g m(-3).

Multiple land-use fugacity model to assess the transport and fate of polycyclic aromatic hydrocarbons in urban and suburban areas

Soil types and land cover can significantly affect the polycyclic aromatic hydrocarbons (PAHs) environmental fate in soil compartments. Hence, estimation of the potential risk of PAHs should be carried out on a smaller scale. Herein, we proposed the multiple land-use fugacity (MLUF) model to investigate the transport and distribution of PAHs in the environment and to provide a more specific and detailed understanding of PAHs dispersion in a multiple soil compartments area. Both steady-state and dynamic MLUF model are implemented use a case study of Beijing. The results indicate that organic films have the greatest concentration of PAHs (6.19 × 103 mg/m3), while the soil and sediment phases retain the majority of PAHs (1.5 × 104 kg and 1.47 × 104 kg, respectively). The potential cancer risk associated with PAHs varies by land-use in the following order: urban green space > agricultural area > forest and semi-nature area. Additionally, the dynamic fluctuation in PAHs concentration was estimated during the COVID-19 pandemic which caused by quarantine indicates that PAH in urban green space soil compartments is more stable than other soil compartments. The present study gives a more scientific understanding of the contaminant transfer and distribution of typical volatile organic compounds in a study area with multiple soil types.

National emissions inventory and future trends in greenhouse gases and other air pollutants from civil airports in China.

Civil aviation is an important source of air pollutants, but this field has received insufficient attention in China. Based on the standard emissions model of the International Civil Aviation Organization (ICAO) and actual flight information from 241 airports, this study estimated a comprehensive emissions inventory for 2010-2020 by considering the impacts of mixing layer height. The results showed that annual pollutant emissions rapidly trended upward along with population and economic growth; however, the emissions decreased owing to the impacts of the COVID-19 pandemic. In 2020, the emissions of carbon monoxide (CO), nitrogen oxides (NOX), particulate matter (PM), methane (CH4), nitrous oxide (N2O), carbon dioxide (CO2), and water vapor (H2O) were 34.34, 65.73, 0.10, 0.34, 0.40, 14,706.26, and 5733.11 Gg, respectively. The emissions of total volatile organic compounds (VOCs) from China's civil airports in 2020 were estimated at 17.20 Gg; the major components were formic acid (1.70 Gg), acetic acid (1.62 Gg), 1-butylene (1.03 Gg), acetone (0.96 Gg), and acetaldehyde (0.93 Gg). The distribution of pollutant emissions was consistent with the level of economic development, mainly in Beijing, Guangzhou, and Shanghai. In addition, we estimated future pollution trends for the aviation industry under four scenarios. Under the comprehensive scenario, which considered the impacts of economic growth, passenger turnover, cargo turnover, COVID-19, and technological efficiency, the levels of typical pollutants were expected to increase by nearly 1.51-fold from 2010 to 2035.

Contributions of Traffic and Industrial Emission Reductions to the Air Quality Improvement after the Lockdown of Wuhan and Neighboring Cities Due to COVID-19.

Wuhan was locked down from 23 January to 8 April 2020 to prevent the spread of the novel coronavirus disease 2019 (COVID-19). Both public and private transportation in Wuhan and its neighboring cities in Hubei Province were suspended or restricted, and the manufacturing industry was partially shut down. This study collected and investigated ground monitoring data to prove that the lockdowns of the cities had significant influences on the air quality in Wuhan. The WRF-CMAQ (Weather Research and Forecasting-Community Multiscale Air Quality) model was used to evaluate the emission reduction from transportation and industry sectors and associated air quality impact. The results indicate that the reduction in traffic emission was nearly 100% immediately after the lockdown between 23 January and 8 February and that the industrial emission tended to decrease by about 50% during the same period. The industrial emission further deceased after 9 February. Emission reduction from transportation and that from industry was not simultaneous. The results imply that the shutdown of industry contributed significantly more to the pollutant reduction than the restricted transportation.

Establishing a link between fine particulate matter (PM2.5) zones and COVID -19 over India based on anthropogenic emission sources and air quality data.

The spread of coronavirus disease of 2019 (COVID-19) pandemic around the globe is affecting people. The majority of Indian urban complexes are reeling under high emissions of deadly fine particulate matter PM2.5 and resulting in poor air quality. These fine particles penetrate deep into the body and fuel inflammation in the lungs and respiratory tract, leading to the risk of having cardiovascular and respiratory problems, including a weak immune system. In the present study, we report the first national-scale study over India, which establishes a strong relationship between the PM2.5 emission load and COVID-19 infections and resulting deaths. We find a significant correlation (R2 = 0.66 & 0.60) between the states as well as districts having varied levels of PM2.5 emissions with corresponding COVID-19 positive cases respectively, and R2 = 0.61 between wavering air quality on a longer time scale and the number of COVID-19 related deaths till 5 November 2020. This study provides practical evidence that cities having pollution hotspot where fossil fuel emissions are dominating are highly susceptible to COVID-19 cases.

A high-resolution typical pollution source emission inventory and pollution source changes during the COVID-19 lockdown in a megacity, China.

To control the spread of COVID-19, China has imposed national lockdown policies to restrict the movement of its population since the Chinese New Year of January 2020. In this study, we quantitatively analyzed the changes of pollution sources in Shanghai during the COVID-19 lockdown; a high-resolution emission inventory of typical pollution sources including stationary source, mobile source, and oil and gas storage and transportation source was established based on pollution source data from January to February 2020. The results show that the total emissions of sulfur dioxide (SO2), nitrogen oxides (NOx), particulate matter (PM), and volatile organic compounds (VOCs) were 9520.2, 37,978.6, 2796.7, and 7236.9 tons, respectively, during the study period. Affected by the COVID-19 lockdown, the mobile source experienced the largest decline. The car mileage and oil sales decreased by about 80% during the COVID-19 lockdown (P3) when compared with those during the pre-Spring Festival (P1). The number of aircraft activity decreased by approximately 50%. The impact of the COVID-19 epidemic on industries such as iron and steel and petrochemicals was less significant, while the greater impact was on coatings, chemicals, rubber, and plastic. The emissions of SO2, NOx, PM2.5, and VOCs decreased by 11%, 39%, 37%, and 47%, respectively, during P3 when compared with those during P1. The results show that the measures to control the spread of the COVID-19 epidemic made a significant contribution to emission reductions. This study may provide a reference for other countries to assess the impact of the COVID-19 epidemic on emissions and help establish regulatory actions to improve air quality.

[Contributors to Air Pollutant Emission Changes in Autumn and Winter in Beijing-Tianjin-Hebei and Surrounding Areas].

Based on the air pollution emission inventory technical methodology, this study conducted a quantitative analysis on the changes in major air pollutant emissions in Beijing-Tianjin-Hebei and its surrounding areas from the 'New Year Haze' in the autumn and winter of 2016-2017 to the 'Pandemic Haze' in the autumn and winter of 2019-2020. The contributions of the implementation of air pollution prevention and control policies and the COVID-19 pandemic to major air pollutant emission reductions were studied, and their impacts on the regional air quality under adverse meteorological conditions were simulated using an air quality model. The results showed that from the 'New Year Haze' in Dec 2016-Jan 2017 to the 'Pandemic Haze' in Jan-Feb 2020, the major air pollutant emissions in the region had dropped by approximately 50%, and the average concentration of PM2.5 was potentially reduced by more than 40% under adverse meteorological conditions. The most effective emission reduction measures included the clean heating project and raising the standards in key industrial sectors, such as the iron and steel industry, coal-fired boilers, and power plants, which contributed 67.1% and 53.4% of the emission reductions in SO2 and PM2.5, respectively. The COVID-19 pandemic predominantly affected the mobile sources and light industry, which contributed 71.9% and 68.2% of the emission reductions in NOx and VOCs, respectively. The implementation of air pollution prevention and control policies contributed substantially to the improvement of regional air quality, which effectively reduced the intensity and extent of the heavy pollution process under unfavorable meteorological conditions. The regional average PM2.5 concentration was reduced by 26%, and the number of days experiencing heavy pollution decreased by 44%. Due to the impacts of the COVID-19 pandemic, the average PM2.5 concentration in the region was reduced by an additional 24%, and the duration and extent of heavy pollution decreased even further.