At the end of 2019, a novel coronavirus COVID-19 emerged in Wuhan, China, and later spread throughout the world, including Iraq. To control the rapid dispersion of the virus, Iraq, like other countries, has imposed national lockdown measures, such as social distancing, restriction of automobile traffic, and industrial enterprises. This has led to reduced human activities and air pollutant emissions, which caused improvement in air quality. This study focused on the analysis of the impact of the six partial, total, and post-lockdown periods (1st partial lockdown from March 1 to16, 2020, 1st total lockdown from March 17 to April 21, 2nd partial lockdown from April 22 to May 23, 2nd total lockdown from May 24 to June 13, 3rd partial lockdown from June 14 to August 19, and end partial lockdown from August 20 to 31) on the average of daily NO2, O3, PM2.5, and PM10 concentrations, as well as air quality index (AQI) in 18 Iraqi provinces during these periods (from March 1st to August 31st, 2020). The analysis showed a decline in the average of daily PM2.5, PM10, and NO2 concentrations by 24%, 15%, and 8%, respectively from March 17 to April 21, 2020 (first phase of total lockdown) in comparison to the 1st phase of partial lockdown (March 1 to March 16, 2020). Furthermore, the O3 increased by 10% over the same period. The 2nd phase of total lockdown, the 3rd partial lockdown, and the post-lockdown periods witnessed declines in PM2.5 by 8%, 11%, and 21%, respectively, while the PM10 increases over the same period. Iraqi also witnessed improvement in the AQI by 8% during the 1st phase of total lockdown compared to the 1st phase of partial lockdown. The level of air pollutants in Iraq declined significantly during the six lockdown periods as a result of reduced human activities. This study gives confidence that when strict measures are implemented, air quality can improve.
This paper examines whether attitudes towards electric vehicles (ATEVs), subjective norms (SNs) and perceived behavioural control (PBC) have significant associations with consumer purchase intention (PI) and the purchase behaviour of environmentally friendly vehicles (EFVs). The results from the survey questionnaires are analysed using confirmatory factor analysis (CFA) and structural equation modelling (SEM). The findings of this paper indicate that ATEV, SN and PBC significantly influence PI. This finding also indicates that environmental consequence and individual preferences do not influence the PI of the respondents. We found that Malaysian car owners are largely unaware of the greenhouse effects on the environment or attach to it little importance, which is reflected in their PI towards EFVs. The outcomes of this study could help policymakers design programmes to influence attitudes, subjective norms, perceived behavioural control and purchase behaviour to prevent further air pollution and reduce CO2 emissions from the transportation sector.
Principal component analysis (PCA) and correlation have been used to study the variability of particle mass and particle number concentrations (PNC) in a tropical semi-urban environment. PNC and mass concentration (diameter in the range of 0.25->32.0 μm) have been measured from 1 February to 26 February 2013 using an in situ Grimm aerosol sampler. We found that the 24-h average total suspended particulates (TSP), particulate matter ≤10 μm (PM10), particulate matter ≤2.5 μm (PM2.5) and particulate matter ≤1 μm (PM1) were 14.37 ± 4.43, 14.11 ± 4.39, 12.53 ± 4.13 and 10.53 ± 3.98 μg m(-3), respectively. PNC in the accumulation mode (<500 nm) was the most abundant (at about 99 %). Five principal components (PCs) resulted from the PCA analysis where PC1 (43.8 % variance) predominates with PNC in the fine and sub-microme tre range. PC2, PC3, PC4 and PC5 explain 16.5, 12.4, 6.0 and 5.6 % of the variance to address the coarse, coarser, accumulation and giant fraction of PNC, respectively. Our particle distribution results show good agreement with the moderate resolution imaging spectroradiometer (MODIS) distribution.
This study assessed the economic value of public urban green spaces (UGSs) in Kuala Lumpur (KL) city by using the hedonic price method (HPM). It involves 1269 house units from eight sub-districts in KL city. Based on the hedonic price method, this study formulates a global and local model. The global model and local model are analyzed using ordinary least square (OLS) regression and geographically weighted regression (GWR). By using the hedonic price method, the house price serves as a proxy for public urban green spaces' economic value. The house price is regressed against the set of three variables which are structural characteristics, neighborhood attributes, and environmental attributes. Measurements of interest in this study are environmental characteristics, including distance to public UGSs and size of public UGSs. The results of the OLS regression illustrated that Taman Rimba Kiara and Taman Tasik Titiwangsa provide the maximum economic value. On average, reducing the distance of the house location to Taman Rimba Kiara by 10 m increased the house price by RM1700. Similarly, increasing the size of the Taman Tasik Titiwangsa by 1000 m2 increases the house price by RM60,000. The advantage of the GWR result is the economic value of public UGSs which can be analyzed by the specific location according to sub-district. From this study, the GWR result exposed that the economic values of Taman Rimba Bukit Kiara and Taman Tasik Titiwangsa were not significant in each of the sub-district within KL city. Taman Rimba Bukit Kiara was negatively significant at all sub-districts except Setapak and certain house locations located at the sub-district of KL. In contrast, Taman Tasik Titiwangsa was positively significant at all sub-districts except certain house locations at the sub-districts of Batu, KL, Setapak, and KL city center. In conclusion, results show that the house price is influenced by the environmental attribute. However, even though both of these public UGSs generate the highest economic value based on distance and size, its significant values with an expected sign are only obtained based on the specific house location as verified by the local model. In terms of model comparison, the local model was better compared with the global model.
Urban road dust contains anthropogenic components at toxic concentrations which can be hazardous to human health. A total of 36 road dust samples from five different urban areas, a commercial (CM), a high traffic (HT), a park (GR), a rail station (LRT), and a residential area (RD), were collected in Kuala Lumpur City followed by investigation into compositions, sources, and human health risks. The concentrations of trace metals in road dust and the bioaccessible fraction were determined using inductively couple plasma-mass spectrometry (ICP-MS) while ion concentrations were determined using ion chromatography (IC). The trace metal concentrations were dominated by Fe and Al with contributions of 53% and 21% to the total trace metal and ion concentrations in road dust. Another dominant metal was Zn while the dominant ion was Ca2+ with average concentrations of 314 ± 190 μg g-1 and 3470 ± 1693 μg g-1, respectively. The most bioaccessible fraction was Zn followed by the sequence Sr > Cd > Cr > Cu > Ni > Co > Mn > As > V > Pb > Fe > Al > U. The results revealed that the highest trace metal and ion concentrations in road dust and in the bioaccessible fraction were found at the LRT area. Based on the source apportionment analysis, the major source of road dust was vehicle emissions/traffic activity (47%), and for the bioaccessible fraction, the major source was soil dust (50%). For the health risk assessments, hazard quotient (HQ) and cancer risk (CR) values for each element were
Human health is threatened by significant emissions of heavy metals into the urban environment due to various activities. Various studies describing health risk analyses on soil and dust have been conducted previously. However, there are limited studies that have been carried out regarding the potential health risk assessment of heavy metals in urban road dust of < 63-μm diameter, via incidental ingestion, dermal contact, and inhalation exposure routes by children and adults in developing countries. Therefore, this study evaluated the health risks of heavy metal exposure via ingestion, dermal contact, and inhalation of urban dust particles in Petaling Jaya, Malaysia. Heavy metals such as lead (Pb), chromium (Cr), zinc (Zn), copper (Cu), and manganese (Mn) were measured using dust samples obtained from industrial, high-traffic, commercial, and residential areas by using inductively coupled plasma mass spectrometry (ICP-MS). The principal component and hierarchical cluster analysis showed the dominance of these metal concentrations at sites associated with anthropogenic activities. This was suggestive of industrial, traffic emissions, atmospheric depositions, and wind as the significant contributors towards urban dust contamination in the study sites. Further exploratory analysis underlined Cr, Pb, Cu, and Zn as the most representative metals in the dust samples. In accommodating the uncertainties associated with health risk calculations and simulating the reasonable maximum exposure of these metals, the related health risks were estimated at the 75th and 95th percentiles. Furthermore, assessing the exposure to carcinogenic and non-carcinogenic metals in the dust revealed that ingestion was the primary route of consumption. Children who ingested dust particles in Petaling Jaya could be more vulnerable to carcinogenic and non-carcinogenic risks, but the exposure for both children and adults showed no potential health effects. Therefore, this study serves as an important premise for a review and reformation of the existing environmental quality standards for human health safety.
By growing urban population, Iran faces numerous environmental issues and solid waste management is on the top of these problems. Studies showed that a daily average of 700-1000 g of wastes are produced per person in Iran, in which organic waste accounts for a significant amount. On the other hand, hospital waste represents a part of the wastes, which need careful consideration from the environmental point of view. In the present study, the amount, composition, and management of urban and hospital wastes were evaluated in 7 Iranian metropolises, which account for about 30% of the population and produce about 35% of the country wastes. Based on prior surveys, landfill method is the current main method for waste management in these cities, which is generally not completely sanitary and therefore causes many environmental problems. The other common methods for waste management in these cities are composting of organic wastes, and the use of waste conversion methods to energy. However, the latter is ongoing only in Tehran which also includes some limitations. Therefore, the study also evaluated the future perspectives and feasibility of waste-to-energy conversion as a promising economic route for waste disposal.
The rapid increase in urbanization has given rise to the need of proper waste management. Within municipal waste, the plastic waste is a growing concern which is causing severe harm to our ecosystem. If ignored, this problem will have harmful effects on both human and wildlife. Therefore, this study aims to find out the factors that influence the recycling behavior patterns of consumers regarding plastic waste. The variables from the theory of planned behavior were adopted to study the behavior of consumers toward recycling plastic waste. The data was collected from 243 residents of Karachi-metropolitan city of Pakistan. The partial least square-structural equation modelling was applied to analyze the data. The findings of the current study reveal that different consumers' attributes and attitudes trigger different types of recycling behavior when it comes to waste disposal. Pressure from family and friends and perceived behavioral control trigger the behavior of reselling the waste plastic products while consumer's awareness of consequences and personal attitude toward proper waste disposal leads to reuse or donating that product to someone who can use that plastic product. The understanding of these consumer attributes may help to shape the behavioral outcomes in order to manage waste disposal. This study will be beneficial for business managers looking to improve reverse logistics as well as government/municipal policy makers and academics/researchers who are interested in a solution-oriented study.
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.
In the current context of rapid development and urbanization, land use and land cover (LULC) types have undergone unprecedented changes, globally and nationally, leading to significant effects on the surrounding ecological environment quality (EEQ). The urban agglomeration in North Slope of Tianshan (UANST) is in the core area of the Silk Road Economic Belt of China. This area has experienced rapid development and urbanization with equally rapid LULC changes which affect the EEQ. Hence, this study quantified and assessed the spatial-temporal changes of LULC on the UANST from 2001 to 2018 based on remote sensing analysis. Combining five remote sensing ecological factors (WET, NDVI, IBI, TVDI, LST) that met the pressure-state-response(PSR) framework, the spatial-temporal distribution characteristics of EEQ were evaluated by synthesizing a new Remote Sensing Ecological Index (RSEI), with the interaction between land use change and EEQ subsequently analyzed. The results showed that LULC change dominated EEQ change on the UANST: (1) From 2001 to 2018, the temporal and spatial pattern of the landscape on the UANST has undergone tremendous changes. The main types of LULC in the UANST are Barren land and Grassland. (2) During the study period, RSEI values in the study area were all lower than 0.5 and were at the [good] levels, reaching 0.31, 0.213, 0.362, and 0346, respectively. In terms of time and space, the overall EEQ on the UANST experienced three stages of decline-rise-decrease. (3) The estimated changes in RSEI were highly related to the changes of LULC. During the period 2001 to 2018, the RSEI value of cropland showed a trend of gradual increase. However, the rest of the LULC type's RSEI values behave differently at different times. As the UANST is the core area of Xinjiang's urbanization and economic development, understanding and balancing the relationship between LULC and EEQ in the context of urbanization is of practical application in the planning and realization of sustainable ecological, environmental, urban, and social development in the UANST.
Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during 30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km2 (Erbil), 217.86 km2 (Baghdad), and 294.43 km2 (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km2) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.
Megacities recently are experiencing a shortage of green spaces basically due to the rapid growth of urbanization and increasing demand for different building types. Consideration of sustainable urban development is essential since the expansion of city facilities should be in line with social, economic, and environmental aspects. In this regard, green roof technology has been recommended as an effective solution for the growth of green spaces per capita and improving sustainability means of urban developments due to its diverse advantages. This study thus aimed at prioritizing sustainability indicators and relative sub-criteria of adopting green roof technology for residential and governmental buildings in the city of Mashhad, Iran, which has a dry climate. For this purpose, thirteen sub-criteria, which are extracted from the existing literature, are classified into three main sustainability indicators (environmental, economic, and social). Also, the best-worth method (BWM) as a multi-criteria decision-making technique was implemented to prioritize indicators and sub-criteria by analyzing the expert's opinion. The results indicated that respective economic and environmental indicators attract the highest priority in residential and governmental buildings. Additionally, the most important sub-criteria in environmental, economic, and social groups are air quality, roof longevity, and public health in both building types, respectively. However, when all criteria were considered, the respective highest priorities belong to roof longevity and air quality in residential and governmental buildings, while biodiversity conservation is the least important one in both building types. The results of this research can be beneficial in other cities with similar economic and climate conditions.
According to the United Nations Agenda, the 2023 sustainable environment is necessary to secure this planet's future; public-private partnerships investment in energy is crucial to sustainable development. The research examines the quantile association between public-private partnership ventures in energy and environmental degradation in ten developing nations, and data is used from January 1998-December 2016. The advanced econometrics quantile-on-quantile regression approach is used to control the issues of heterogeneity and asymmetric relationship. According to the quantile-on-quantile approach, there is a strong positive association between public-private partnerships in energy and environmental degradation in Argentina, Brazil, Bangladesh, and India. But the negative relationship is observed on different quantiles of China, Malaysia, Mexico, Peru, Thailand, and the Philippines. The findings suggest that the world needs to act as a single community and divert its resources toward renewable energy sources to control climate change; also, to accomplish the UN 15-year road map of Agenda 2023 with 17-SDGs; out of these 17 sustainable goals, SDG-7 is related to affordable and clean energy, SDG-11 is about sustainable cities and communities, and SDG-13 focuses on climate action for sustainable development.
The impact of global warming presents an increased risk to the world's shorelines. The Intergovernmental Panel on Climate Change (IPCC) reported that the twenty-first century experienced a severe global mean sea-level rise due to human-induced climate change. Therefore, coastal planners require reasonably accurate estimates of the rate of sea-level rise and the potential impacts, including extreme sea-level changes, floods, and shoreline erosion. Also, land loss as a result of disturbance of shoreline is of interest as it damages properties and infrastructure. Using a nonlinear autoregressive network with an exogenous input (NARX) model, this study attempted to simulate (1991 to 2012) and predict (2013-2020) sea-level change along Merang kechil to Kuala Marang in Terengganu state shoreline areas. The simulation results show a rising trend with a maximum rate of 28.73 mm/year and an average of about 8.81 mm/year. In comparison, the prediction results show a rising sea level with a maximum rate of 79.26 mm/year and an average of about 25.34 mm/year. The database generated from this study can be used to inform shoreline defense strategies adapting to sea-level rise, flood, and erosion. Scientists can forecast sea-level increases beyond 2020 using simulated sea-level data up to 2020 and apply it for future research. The data also helps decision-makers choose measures for vulnerable shoreline settlements to adapt to sea-level rise. Notably, the data will provide essential information for policy development and implementation to facilitate operational decision-making processes for coastal cities.
Under the new development model, the digital economy has become a new engine to promote the green development of the economy and achieve the goal of "double carbon." Based on panel data from 30 Chinese provinces and cities from 2011 to 2021, the impact of the digital economy on carbon emissions was empirically studied by constructing a panel model and a mediation model. The results show that firstly, the effect of the digital economy on carbon emissions is a non-linear inverted "U" shaped relationship, and this conclusion still holds after a series of robustness tests; secondly, the results of the benchmark regression show that economic agglomeration is an essential mechanism through which the digital economy affects carbon emissions and that the digital economy can indirectly suppress carbon emissions through economic agglomeration. Finally, the results of the heterogeneity analysis show that the impact of the digital economy on carbon emissions varies according to the level of regional development, and its effect on carbon emissions is mainly in the eastern region, while its impact on the central and western regions is weaker, indicating that the impact effect is primarily in developed regions. Therefore, the government should accelerate the construction of new digital infrastructure and implement the development strategy of the digital economy according to local conditions to promote a more significant carbon emission reduction effect of the digital economy.
This study examined (1) the effect of environmental concern, environmental knowledge, and health consciousness on attitude towards eBikes; (2) the effect of eBikes attributes of interest and infrastructure on perceived behavioural control; and finally, (3) the effect of attitude towards eBikes, subjective norms, and perceived behavioural control on eBikes commuting intention and its usage among Malaysian youth. This study adopted a cross-sectional design and convenience sampling, and collected quantitative data from 699 Malaysian youth through an online survey. Findings revealed that (1) environmental concern, environmental knowledge, and health consciousness had a positive and significant impact on attitude towards eBikes; (2) eBikes attributes of interest and infrastructure were positively and significantly related to perceived behavioural control; (3) attitude towards eBikes, subjective norms, and perceived behavioural control had a positive and significant effect on eBikes commuting intention; (4) eBikes commuting intention was positively and significantly related to the usage of eBikes; (5) eBikes commuting intention significantly mediated the relationship between attitude towards eBikes and perceived behavioural control on the usage of eBikes among Malaysian youth; and (6) eBikes commuting intention did not mediate the relationship between social norm and usage of eBikes. Although environmental knowledge and social norm are proven to have a positive and significant relationship, this analysis demonstrated a relatively low effect size. To promote environmental and sustainable development in cities through the mass adoption of eBikes among Malaysian youth, policymakers should highlight the benefits of using eBikes, introduce proper policies, and involve the development of improved and connected cycling paths in the city sustainable infrastructure plans.
Bioretention systems have been implemented as stormwater best management practices (BMPs) worldwide to treat non-point sources pollution. Due to insufficient research, the design guidelines for bioretention systems in tropical countries are modeled after those of temperate countries. However, climatic factors and stormwater runoff characteristics are the two key factors affecting the capacity of bioretention system. This paper reviews and compares the stormwater runoff characteristics, bioretention components, pollutant removal requirements, and applications of bioretention systems in temperate and tropical countries. Suggestions are given for bioretention components in the tropics, including elimination of mulch layer and submerged zone. More research is required to identify suitable additives for filter media, study tropical shrubs application while avoiding using grass and sedges, explore function of soil faunas, and adopt final discharged pollutants concentration (mg/L) on top of percentage removal (%) in bioretention design guidelines.
The construction of low-carbon cities is an essential component of sustainable urban development. However, there is a lack of a comprehensive low-carbon city design and evaluation system that incorporates "carbon sink accounting-remote sensing monitoring-numerical modelling-design and application" in an all-around linkage, multi-scale coupling, and localized effects. This paper utilizes the Citespace tool to evaluate low-carbon city design applications by analyzing literature in the Web of Science (WOS) core collection database. The results reveal that low-carbon cities undergo four stages: "measurement-implementation-regulation - management." The research themes are divided into three core clustering evolutionary pathways: "extension of carbon sink functions," "spatialisation of carbon sink systems," and "full-cycle, full-dimensional decarbonisation." Applications include "Utility studies of multi-scale carbon sink assessments," "Correlation analysis of carbon sink influencing factors," "Predictive characterisation of multiple planning scenarios," and "Spatial planning applications of urban sink enhancement." Future low-carbon city construction should incorporate intelligent algorithm technology in real-time to provide a strong design basis for multi-scale urban spatial design with the features of "high-precision accounting, full-cycle assessment and low-energy concept."
In recent years, academics have paid more attention to green finance, and public companies have reached a broad consensus on the concept of timely environmental, social, and governance (ESG) disclosure. Due to the close relationship between green finance and ESG, this presents an opportunity to determine whether green finance compels companies to actively disclose ESG. The sample for this study consists of China's non-financial A-share listed companies from 2010 to 2021, and the empirical findings demonstrate that green finance can positively influence the ESG performance of listed companies. Through an analysis of heterogeneity, this study reaches the following conclusions: state-owned enterprises, heavy pollution companies, and companies in low-carbon pilot cities perform better in terms of green finance's role in promoting ESG scoring. This study also introduces market concentration and social trust as the moderating variables, enriching the green finance research framework. Through the analysis of moderating variables, the 'black box' effect of green finance on ESG is disclosed, providing theoretical support for the government and companies to better comprehend the policy effect as well as a reference for reform and experimental promotion of green finance.
A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.