Assessment of Health Impacts of PM2.5 on the Vulnerable Groups in the Central part of Bangladesh

Assessment of Health Impacts of PM2.5 on the Vulnerable Groups in the Central part of Bangladesh | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Health Impacts of PM2.5 on the Vulnerable Groups in the Central part of Bangladesh Shareful Hassan, Md. Tariqul Islam, Mohammad Amir Hossain Bhuiyan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1605575/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Particulate matter (PM 2.5 ) is one of the critical sources for outdoor air pollution and poses the most significant public health threat. In Bangladesh, particularly in the major urban cities, PM 2.5 has been identified as a significant public health hazard. This research aims to perform a spatiotemporal mapping of PM 2.5 from 2002–2019 to identify the hotspots in central Bangladesh and to estimate the health impacts on pregnant women and population aged 60 + . A time-series of remotely sensed PM 2.5 is used in hotspot analysis, applying Geographic Information Systems (GIS). To explore the health impacts due to PM 2.5 , a questionnaire survey is conducted on pregnant women and population aged 60 + in both high- and low-spots zones. A non-parametric statistical analysis is conducted to understand the mean impacts. The findings of this research reveal that the annual concentration of PM 2.5 is increased by 47% during 2002–2019. Most of the high hotspot zones are identified in the middle of the study areas; the core urban areas of Dhaka, Narayanganj, and Gazipur Districts. The traffic vehicles, urbanization, construction and brickfield activities, and industrial emissions are the main controlling factors for increasing PM 2.5 . Further, the health impacts of both pregnant and population 60 + are higher in the high-spot zone than in the low-spot area. Note that pregnant women have less PM 2.5 related information than 60 + population in both high- and low-spot zones. On the policy applications, the relevant departments may utilize these findings for health hazard risk reduction and local and regional air pollution mitigation. Bangladesh Air pollution in Dhaka GIS MODIS recorded PM2.5 Spatiotemporal PM2.5 Geostatistical analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Particulate Matter (PM 2.5) is one of the primary pollutants for ambient air pollution, which often imposes the greatest threat to public health, causing about 4.2 million global deaths a year (WHO 2016 ; Landrigan et al. 2018 ). The PM 2.5 (diameter < 2.5 µm) has been exposed as the fundamental biological and environmental aspects by creating an adverse impact on regional and local public health (Autrup 2010 ). Long-term and short-term exposures to the high-level concentration of PM 2.5 are correlated with various public health problems, including death, respiratory difficulty, coronary disease, lung cancer, cardiac pain, asthma, and skin problem predominantly in urban and peri-urban areas (Andersen et al. 2012 ; Hoek et al. 2013 ; Raaschou-Nielsen et al. 2013 ; Beelen et al. 2014 ; Dirgawati et al. 2016 ; Chen et al. 2018 ). The most vulnerable population groups are children under aged five years, pregnant women, and the elderly (60 + ) who are sensitive to a high level of PM 2.5 due to many health issues (Luo et al. 2018 ; Lei et al. 2019 ; Zeng et al. 2020 ). The PM 2.5 has been considered as one of the leading air pollutants in Dhaka and its adjacent areas, which has also been evidenced as an inevitable threat to human health as well as all living organisms (Kim et al. 2015 ; Liang et al. 2016 ). It happens because of a large share of air (⁓58% of total PM 2.5 ) of Dhaka and its adjacent areas are mixed by the toxic gasses mainly from brickfields operated in and around Dhaka (Begum et al. 2013 ). Moreover, other reasons are motor vehicles (10.4%), road dust (7.70%), fugitive Pb (7.63%), soil dust (7.57%), biomass burning (7.37%), and sea salt (1.33%) (Begum et al. 2013 ). In addition, the physical signature of PM 2.5 is being increased gradually because of unplanned rapid urbanization and swift industrialization to boost the country’s economy by generating the most unexchanged cost of environmental pollution (Zhang and Zhang 2018). However, scrutiny through the PM 2.5 in Bangladesh, China, India, and Pakistan, it is found that around 86% of populations are exposed to the most extreme level (i.e., > 75 µg/m 3 ) of pollution concentration (HEI 2017 ). If the daily concentration level of PM 2.5 increases by ten µg/m 3 , the prevalence of respiratory and other health problems increases by 2.07%, while the hospital admission rate is increased by 8% (Dominici et al. 2006 ; Zanobetti et al. 2009 ; Xing et al. 2016 ). According to the World Bank report of 2018, every year, around seven million premature deaths occur worldwide due to PM 2.5 in which 234,000 deaths (3.34%) are recorded in Bangladesh (World Bank 2018 ; PPI 2017 ). Geographic Information System (GIS), together with remote sensing techniques, is a widely used method for Spatiotemporal hotspots analysis of PM 2.5 (Hoque et al. 2014 ; Cao et al. 2018 ). This research aims to (i) perform a spatiotemporal mapping applying GIS using remotely sensed pixel-based time series PM 2.5 data considering a broader scale geographical context in Bangladesh (ii) measure the health effects of most vulnerable population groups such as pregnant women and population 60 + using primary health information, a self-reported stakeholders’ perception contrasting very high and low concentrations of PM 2.5 from hotspot areas, and (iii) test a hypothesis that pregnant women and the 60 + population in the high-spot zones are more vulnerable than low-hot spot areas caused of the high concentration of PM 2.5 . Previous Studies On Pm And Its’ Impact On Public Health Globally, the remote sensing data coupled with primary health information have been used widely in air pollution-related health research to estimate the spatiotemporal pattern of PM 2.5 , the spatial extent of a hotspot, and impacts on public health, particularly on vulnerable groups. Hoque et al. ( 2014 ) and Cao et al. ( 2018 ) conducted such a study in the US, Europe, China, and India using Moderate Resolution Imaging Spectroradiometer (MODIS) data without validation with the ground or relevant data and linked the impact of PM 2.5 concentration and its exposure to different population groups. The validation process enhances the applicability of integrated MODIS and health data for the decision-making process. Knowing the spatial trend and geographical distribution of PM 2.5 is a vital policy aspect for the air pollution control mechanism. To understand this, Zhao et al. ( 2019 ) used a pixel-based liner pattern of PM 2.5 in China using 18 years MODIS data and energy consumption. But only energy consumption is not a major contributing factor to measure the increasing trend of PM 2.5, which is a limitation. Several visible and contributing factors from development and anthropogenic aspects may consider in understanding the real situation of an increasing trend. Hu et al. ( 2014 ) suggested that the concentration of PM 2.5 depends on different geographic locations and land use classes. Using a Spatiotemporal Model, they found that the spatial trend of PM 2.5 concentration is more in urban areas than in rural or hilly areas (Hu et al. 2014 ). Used parameters in modeling were limited to five, along with PM 2.5 . However, running a robust model, different important variables, e.g., topographical, environmental, micro-climate, and anthropogenic, may generate more authentic results. Perception about air pollution, particularly on PM 2.5, is a critical aspect in terms of public health research because people need to be aware of the adverse impact of PM 2.5 . Jiang et al. ( 2016 ) investigated public awareness on smog pollution in rural China and founded that the perception about air pollution at the individual level was much better. However, this study would have been more substantial if they could use children, pregnant women, and older people as critical respondents. Urban and urban slums are most vulnerable due to PM 2.5 within the urban context. Egondi et al. ( 2013 ) conducted a cross-sectional study considering people ages 35 + in Nairobi to collect and analyze the perception of air pollution for designing appropriate intervention strategies. The sample size being the representative of this study, it did not consider any parametric or non-parametric statistical analysis to compare their results with any control data, a limitation. However, Pithon ( 2013 ) suggests that the control group is essential, and it resembles the impact between two groups with scientific evidence. Besides, Cao et al. ( 2018 ) show a robust correlation between PM 2.5 and mortality of lunch cancer in China, applying a statistical regression model using the old mortality data of 2008. Recent data of a variable can enhance the applicability of a regression model. Miller and Xu ( 2018 ) also suggest that panel or primary data are substantial to make a statistical relationship with PM 2.5 . Importantly, primary data can give real scenarios from the respondents, particularly from vulnerable groups. In the context of Bangladesh, a wide range of research for establishing a nexus between PM 2.5 and public health has been conducted in and around Dhaka city (Salam et al. 2008 ; Begum et al. 2010 , 2013 ; Azkar et al. 2012 ; Hoque et al. 2014 ; Begum 2016 ; Rana et al. 2016 ). Most of the studies were conducted in smaller geographic areas using handheld PM 2.5 sampling machines’ data from only three ground stations, and the primary health data of vulnerable groups considering hotspot zones were ignored. However, to generalize the picture of the concentration of PM 2.5, only three stations data, particularly in Dhaka Mega City in Bangladesh and its impact on health hazards, may be misleading. Therefore, in this study, a broader scale geographic area is selected considering MODIS data combine with peoples’ perception of the vulnerable group that enhances the insight into a better understanding of PM 2.5 concentration and its impact on public health. Study Area The study area of this research is located in the Dhaka Division of Bangladesh, covering its five central industrial Districts; Dhaka, Narayanganj, Munshiganj, Narsingdi, and Gazipur (Fig. 1 ). The entire geographic area lies between 23°20'N-24°20'N latitudes and 90°00'E-91°00'E longitudes, which covers about 6,043 square kilometers, including 22,066,710 populations (Fig. 1 ). Having a tropical wet monsoon and dry winter climate, the study area has an annual average rainfall of 1,854 mm with an average yearly temperature of 25 0 C (Hossain and Bahauddin 2013 ). This study area was selected due to some pragmatic reasons: (a) colossal population pressure, (b) massive industrial developments, (c) higher level of traffic concentration, (d) internal migration, and (e) unplanned urban products, which are the key controlling factors for its local and regional atmospheric conditions. Some scientists mentioned that this area has the largest density of industrialization due to easy access to finance, enormous transportation facilities, location-based advantage, spatial context, and different management services (Islam 2000 ). Many industries operate activities in the study area, which is the key triggering reasons to produce enormous emission and gaseous particulates (Salam et al. 2008 ). These industries include, e.g., ready-made garment, textile, pharmaceuticals, cement, brickfields, fertilizer, assembling of a motorcycle, bus, truck, Compress Natural Gas, raw material processing, food and sugar, and electrical power. Methods And Results This study used the annual average data of PM 2.5 between 2002–2016 and 2019 for the quantitative measures. The data for 2017 and 2018 are missing here. To understand the impact of PM 2.5 on public health, a structured questionnaire survey was conducted, a qualitative analysis. Details of the methods are described here. Retrieving PM 2.5 data The annual average data of PM 2.5 were collected as raster-ASCII format with a 0.01 X 0.01 deg spatial resolution from Van Donkelaar et al. ( 2016 ), a study group at the Atmospheric Composition Analysis Group of Dalhousie University, Canada. They derived the PM 2.5 from Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and SeaWiFS sensors. A robust Geographically Weighted Regression (GWR) method along with GEOS-Chem Models to simulate the spatiotemporal variations across the world, was applied (Van Donkelaar et al. 2016 ). The raster data were converted to point feature data within the study area using a district boundary (shapefile) as a mask applying open-source GIS software, QGIS 3.14 (QGIS 2016 ) where each pixel generated one point feature. The shapefile (mask) was collected from Bangladesh Local Government and Engineering Department (LGED 2020 ) with a coordinate reference system, World Geodetic System 1984 (WGS84). The converted point features were further used for geostatistical, hotspots, and risk zone analysis. PM 2.5 data validation The PM 2.5 derived from satellite images were validated using ground stations’ data during 2002–2019 from the Department of Environment, Government of Bangladesh (CASE 2019 ). There are only five ground stations (Fig. 1 ) available in the study area. The annual average MODIS and ground measured PM 2.5 data were used in a statistical correlation (Ni et al. 2018 ). The Correlation Coefficient ( R 2 ) and the Adjusted Correlation Coefficient were estimated (Fig. 2 ). The extracted PM 2.5 data from MODIS provided a good fit with the ground base measurement as R 2 = 92.05% (Fig. 2 ). It revealed that the derived PM 2.5 data were estimated with high accuracy. Multivariable and Spatio-temporal analysis Temporal analysis of basic statistics, e.g., minimum, maximum, and mean value of PM 2.5 during the study period 2002–2019, was calculated. In this period, the mean annual rate of PM 2.5 is increased by ⁓42% in the study area (Fig. 3 a-c). The yearly trend of minimum values of PM 2.5 is increased by 40%, while the maximum value is increased by 37% (Fig. 3 ). The concentration PM 2.5 is almost stable to 60 ± 2 µg/m 3 during 2003–2008 (Fig. 3 b). The highest variation of PM 2.5 is 8%, found from 2012 to 2016 (Fig. 3 b). Besides, an upward trend of the mean values is observed from 2013 to 2019 (Fig. 3 b), and the highest (Dhaka District) and lowest (Narsingdi District) increment happen with the gradient of 1.82 and 1.74, respectively (Fig. 3 d, h). All these statistical values exceed the annual standard limit of the World Health Organization (WHO) that is 15 µg/m 3 (Fig. 3 a) (WHO 2016 ). A time-series mapping was created using a specific year's average values of PM 2.5 between 2002 and 2019 to visualize the spatiotemporal trend of PM 2.5 (Fig. 4 ). However, to identify the most pollutant and affected zones in the study area, a general map was prepared using average value considering the entire study period (2002–2019), the average map in Fig. 4 . In the Dhaka District, the average annual PM 2.5 is 65–67 µg/m 3 while it is 62–65 µg/m 3 in Narayanganj, 60–66 µg/m 3 in Gazipur, 61–64 µg/m 3 Narshingdi, and 63–67 µg/m 3 in Munshiganj Districts (Fig. 4 ). The Dhaka District, the central part of the study area, has more signatures of air pollution than other parts. Predominantly, all urban cities of the middle part have higher concentrations of PM 2.5 . On the other hand, the northern and southern parts of the study area have less pollution because of peri-urban and less industrial and brickfield activities (Fig. 4 ). Hotspot analysis The spatial process of the statistical clustering method was considered to identify the concentration of PM 2.5 pollutants in the long-term spatiotemporal pattern of air pollution (e.g., Habibi et al. 2017 ). In ArcsGIS, the Hot Spot Analysis tool in Spatial Statistics was applied to find out the hottest and coldest areas. In the hotspot analysis, Getis–Ord Gi*cluster statistic method was selected as a local spatial statistic using average temporal vector data (point feature) of PM 2.5 . The Gi*cluster statistic works based on the weights and heterogeneity in each data point of PM 2.5 (Songchitruksa and Zeng 2010 ). The Gi* statistic uses the following measures as mentioned by Environmental Systems Research Institute (ESRI) (ESRI 2019 ) to identify the hotspots areas: $${G}_{i}^{*}=\frac{\sum _{j=1}^{n}{w}_{i,j}{x}_{j}-\stackrel{-}{X}\sum _{j=1}^{n}{w}_{i,j}}{\sqrt[s]{\frac{\left[n\sum _{j=i}^{n}{w}_{i,j}^{2}-{\left(\sum _{j=1}^{n}{w}_{i,j}\right)}^{2}\right]}{n-1}}}$$ where, x j is the value of j, w i,j is the spatial weight between feature i and j , n is equal to the number of features, \(\stackrel{-}{X}= \frac{\sum _{j=1}^{n}{x}_{j}}{n}\) , and \(s= \sqrt{\frac{\sum _{j=1}^{n}{x}_{j}^{2}}{n}-{\left(\stackrel{-}{X}\right)}^{2}}\) . A Getis–Ord Gi* produces z- scores and p- value. A higher z -score and a small p -value of a cluster signify the hottest spot while a negative z -score and a small p -value present the coldest area (Jana and Sar 2016 ). Readers are encouraged to read (ESRI 2019 ) more about z -score and p -value. The raster overlay was applied using the resultant hotspots areas and area-specific population and most critical public health data to find out the riskiest zones (e.g., Kumar et al. 2015 ). Further, the hotspot map was overlaid with upazlias’ total population (Fig. 5 b), population 0–9 (Fig. 5 c), population 60 + (Fig. 5 d), pneumonia patient (Fig. 5 e), and pregnant women (Fig. 5 f). The upazilas’ specific population data was collected from the Bangladesh Bureau of Statistics (BBS) (BBS 2020 ) to calculate area-specific, most vulnerable population groups whose ages between 0–9 and 60 + years. The upazilas’ specific pregnant and pneumonia patients were collected from Bangladesh Directorate General of Health Services (BDGHS) (DGHS 2019 ). Near about one-third area is found as very high- and high-hotspot zones in the study (Fig. 5 a). The spatial difference between the very high- and high-hotspots zones is almost negligible (Fig. 5 a). Most of these very high-spot zones were found in all city areas of Dhaka, Gazipur sadar , Kaliganj, Rupganj, Sonargaon, Savar, and Dhamrai areas. Moreover, this research found 3640748 persons (16.5% out of total population) (Fig. 5 b) of which 4.39% (969261 persons) are age group 0–9 (Fig. 5 c), 0.95% (210999 persons) are age group 60 + (Fig. 5 d), 5% (12,062,419 persons) are pregnant women (Fig. 5 e), and 1% (24621 persons) are pneumonia patients (Fig. 5 f) in very high- and high-hotspot zones. Impact of PM2.5 on public health The self-reported health impacts due to the PM 2.5 in the very high-, high-hotspots, and low-spots zones, a primary survey was conducted considering 115 sample populations. For this health survey, a purposive sampling method (Palinkas et al. 2015 ) was followed to collect the individual specific in-depth information from each respondent using a mini-structured questionnaire from 26 December 2019 to 27 January 2020. The health impacts between very high- and low-spot zones are compared. A total of 85 samples were conducted to the population age 60 + , of which 55 samples were from very high- and 30 samples were from low-spot zones. On the other hand, 30 samples were conducted to the pregnant women, of which 20 samples were from very high- and ten samples were from low-spot zones. For collecting data about pregnant women, the survey team went to government health facilities and practice chambers of the gynecologist. After getting a consensus from a pregnant woman or her caregivers, data was collected. For the population age 60 + , respondents were selected as one in a ⁓5 km radius to enhance the uniform distribution of the sample. After data collection and editing, significant sources of air PM 2.5 and self-reported health impacts due to PM 2.5 were analyzed using descriptive analysis. A non-parametric Mann-Whitney U (McKnight and Julius 2010 ) test to compare the health impacts between very high- and low-spot zones was conducted. This non-parametric test was selected because these two groups were not normally distributed, and the sample size was sufficiently small, what is one of the limitations of this study. However, this test tends to be more appropriate in this situation (McKnight and Julius 2010 ). Stata version 13 (StataCorp LLC n.d.) was used to conduct different statistical analyses. Respondents’ knowledge about PM 2.5 About 71% of the population age 60 + in the high-spot zone know well about PM 2.5 (Fig. 6 ). Contrary, more than 73% of the same group (60+) do not know about PM 2.5 in the low-spot zone (Fig. 6 ). Only 55% of pregnant women in the high-spot area know about PM 2.5 , while 70% of pregnant women have no idea about PM 2.5 in the low-spot zone. In this analysis, pregnant women have less access to information related to PM 2.5 than the population 60 + in both zones (Fig. 6 ). Respondents’ knowledge of sources of PM 2.5 Population age 60 + believe that road vehicle (64%), urbanization (84%), construction site (43%), brickfield (38%), and industrial emissions (44%) are responsible for PM 2.5 in high-spot zone. Contrary, pregnant women in the high-spot zone suggest that dust (25%), construction site (57%), and brickfield (%) are the predominant controlling factors for PM 2.5 (Table 1 ). In the low-spot zone, both pregnant women and population age 60 + mention that the dust (20%) and industrial emissions (56%) are the triggering factors for increasing PM 2.5 . Table 1 Respondent’s perception about sources of PM 2.5 in both high- and low-spot zones. Pregnant women in low-spot Pregnant women in high-spot 60 + pop in low-spot 60 + pop in high-spot Road vehicle 9.1% 13.6% 13.6% 63.6% Dust 20.0% 25.0% 35.0% 20.0% Urbanization 16.7% 0.0% 0.0% 83.3% Brickfield 3.1% 25.0% 34.4% 37.5% Construction site 0.0% 57.1% 0.0% 42.9% Industrial emission 0.0% 0.0% 56.3% 43.8% Respondents’ responses about health risk due to PM 2.5 Breathing problems, cold/cough, eye problems, skin problems, and asthma are identified as significant health problems in both high- and low-spot zones. The Mann-Whitney U test results suggest that the pregnant women group has a higher mean rank (16.43) in the high-spot than the low-spot zone (13.65) (Table 2 ). The Mann-Whitney U test is estimated to 81.5, and the p-value is to 0.422, which is higher than 0.05. So, the test is not statistically significant, but there the difference exists. Table 2 Respondent’s self-reported health risk due to particulate matter Groups Low spot zone High spot zone Mann-Whitney U p n Mean rank n Mean rank Pregnant women 10 13.65 20 16.43 81.5 0.422 60 + population 30 33.35 55 48.26 535.5 0.006* * Statistically significant at 0.05 However, a higher mean rank of 48.26 for the population 60 + is calculated in the high-spot, compared to that in the low-spot zone to 33.35. Besides, the Mann-Whitney U test is 535.5, and the p-value is 0.006., which is less than 0.05 (Table 2 ). It proves that there is a significant difference in the health impact of pregnant women and population 60 + between the high- and the low-spot zones. The health effects of pregnant women and population 60 + in high-spot regions are more significant than low-spot areas that satisfy the hypothesis. Discussions For better understanding the effects of PM2.5 on pregnant women and 60 + population health, this study is unique, particularly in the context of Bangladesh in many ways; e.g., (i) a large scale geographic area are considered what is usually ignored in many pieces of research (Begum et al. 2010 , 2013 ; Azkar et al. 2012 ; Hoque et al. 2014 ; Hu et al. 2014 ; Rana et al. 2016 ; Begum 2016 ; Cao et al. 2018 ; Rahman et al. 2019 ; Zhao et al. 2019 ), (ii) remotely sensed PM 2.5 data is used that can be applied as an independent data source of PM 2.5 (Cao et al. 2018 ; Zhao et al. 2019 ), however, it is further validated using ground-based stations’ data, and (iii) qualitative measures by stakeholders’ perceptions are combined with the quantitative measures that are very important in this particular issue (Egondi et al. 2013 ; Hoque et al. 2014 ). A group of researchers conducted a validation of extracted PM 2.5 values from satellite images with ground station data, resulting in R 2 = 0.54% in Beijing, China, which is less than this study (⁓0.92%) (Ni et al. 2018 ). It is because the Chinees research group has used a very scattered location of many ground stations’ data (Ni et al. 2018 ). Han et al. ( 2018 ) found a very high R 2 -value ranging from 0.72–0.97% using 35 ground-based monitoring stations data. Therefore, it is recommended to use many and scattered locations of ground stations’ data for statistical validation of satellite recorded PM 2.5 . However, validity estimation also depends on regional topography and weather patterns, e.g., relative humidity, atmospheric temperature, wind speed, and sessional climate variability (Al-Hamdan et al. 2019 ). Estimation of the spatiotemporal concentration of PM 2.5 is a critical issue for managing local and regional atmospheric pollution strategy as well as a public health concern. The average annual concentration of PM 2.5 is increased by 42% during 2002–2019 (Fig. 3 ). It is because of excessive emissions of different kinds of diesel and petrol vehicles as well as poorly maintained automobiles, that are generating PM 2.5 pollutant in urban areas of Bangladesh (Begum 2016 ). Begum et al. ( 2013 ) suggest that the motor vehicles (10.4%), road dust (7.70%), fugitive Pb (7.63%), soil dust (7.57%), biomass burning (7.37%), and sea salt (1.33%) are responsible for PM 2.5 in the Dhaka city and its adjacent areas what is similarly responded by the stakeholders in this study (Table 1 ). In Bangladesh and its megacities like Dhaka, ⁓35% of ambient PM 10 and ⁓15% of PM 2.5 are being generated from brick kiln emissions and transportation systems (Motalib and Lasco 2015 ). Comparable with China (60 µg/m 3 ), Bangladesh (77 µg/m 3 ) generates a higher level of PM 2.5 in 2016 even though both countries have a similar pattern of population growth (Cao et al. 2018 ). Likewise in Bangladesh, ⁓88% of the areas in China had an increasing trend of PM 2.5 in the last 18 years due to huge traffic, transportation, and industrialization (Zhao et al. 2019 ), that is also similar in India (Kandlikar and Ramachandran 2000 ). However, the dominant factors for increasing the concentration of PM 2.5 in Vietnam are agriculture, cooking, heating, construction, and urbanization (Nguyen et al. 2018). However, the concentration of PM 2.5 in the atmosphere depends on several anthropogenic factors such as transportation, industrial developments, and cooking and heating activities (Gautam et al. 2016 ; Al-Hamdan et al. 2019 ). It also depends on some meteorological factors like wind speed, air relative humidity, cloud cover, and ambient temperature (Al-Hamdan et al. 2019 ). They suggest a large geographic area for investigation, and it is considered in this study. The results of this study reveal that the areas i.e. Dhaka, Narayanganj and Gazipur have more anthropogenic sources like manufacturing factories, high traffic congestion, and other combustion activities, ultimately leading to relatively a higher annual PM 2.5 concentration which is similar with the PM 2.5 concentration in the other developing countries like, China, India, Iran, and Tanzania (Mkoma et al. 2010 ; Tiwari et al. 2015 ; Arfaeinia et al. 2016 ). The other two study areas, the Narsingdi and Munshiganj Districts have a relatively low level of PM 2.5 concentration (Figs. 4 , 5 a). The central part of the study area has been found in a higher concentration of PM 2.5 than the north and southern parts (Figs. 4 , 5 a). However, incorporation of meteorological factors and seasonal variations could give more precise information about the concentration of PM 2.5 fluctuation instead of just depending on annual average concentration which could often be misleading to describe the short-term anthropogenic activities or weather conditions, such as in Beijing–Tianjin–Hebei regions of China (Rajput et al. 2013 ; Mangal et al. 2018 ). There are more than 1,850 ground-based air pollution monitoring stations in the European cities and among all, the sources of the maximum concentration of PM 2.5 of 12 cities are traffic-related (Kiesewetter et al. 2015 ), what is also similar in Bangladesh. Note that, the Saharan desert advection in the Mediterranean area (Adães and Pires 2019 ), and the relative humidity with the traffic dust in Sacramento and California in the USA is the dominant factor for PM 2.5 (Mukherjee et al. 2019 ) what may not be comparable with PM 2.5 in this study. PM 2.5 is one of the significant public health concerns in urban and peri-urban areas of Bangladesh (Rahman et al. 2019 ). The excessive standard threshold of PM 2.5 significantly impacts to vulnerable population groups, particularly pregnant women and population 60 + (Miller and Xu 2018 ). The higher concentration of PM 2.5 and its adverse effects on the urban community is exposed as a common public health problem in Bangladesh (Begum et al. 2013 ). Most of the public health problems are identified as pulmonary, cardiovascular, cancer, diabetics, chronic respiratory, low birth, weight, and premature death (Lawal and Asimiea 2015 ). Generally, pregnant women and population 60 + who are living in urban areas can be affected by the different respiratory and non-communicable diseases in many ways (Luo et al. 2018 ). The significant health complexity of pregnant women and pneumonia patients is high in the high-spot zones (Fig. 5 e-f ), which is similarly found in China (Andersen et al. 2012 ; Arroyo et al. 2019 ). In Nanjing, China, the concentration of PM 2.5 is responsible for high incidences of premature death (Li et al. 2019 ), and in 2015, total premature death in China was recorded to 341,701 persons for stroke and 67,325 persons for lower respiratory infection linked to PM 2.5 concentration (Wang et al. 2019 ). By executing the hypothesis test, this research suggests that pregnant women’s health is more sensitive to the effects of PM 2.5 in the high-spot region than population 60 + in the study area, which is similar in a developed country, like in Spain (Arroyo et al. 2019 ). Nevertheless, public perception of PM 2.5 is a critical issue, and a better understanding of the impact of PM 2.5 may reduce health burden in Asia and South Asia (Jiang et al. 2016 ; Cao et al. 2018 ; Achakulwisut et al. 2019 ). The pregnant women both in the high- (55%) and low-spot zone (30%) in this study area know little about the impact of PM 2.5 compared to population age 60 + (Fig. 6 ). Gender gaps in Bangladesh may cause it, similarly, found in the case of public information and awareness for pre-disaster preparedness for a tropical cyclone (Röhr 2006). The stakeholders’ perceptions in this study suggest that the awareness activities on PM 2.5 and air pollution happen more often in urban areas than in rural areas. Therefore, people are more informed about PM 2.5 in urban areas (high-spot zones) than the peri-urban area (low-spot zones). Besides, urban people have more means of getting air pollution-related information, e.g., high mobility, accessible information, mobile information desk, or portal than rural or peri-urban areas (Qian et al. 2016 ). However, the perception of air pollution depends not only on physical factors but also on employment, health, wages, place, and working environment (Achakulwisut et al. 2019 ). The urbanization, road vehicles, brickfield and construction activities, and industrial emissions are the key controlling factors for increasing PM 2.5 in the study area. These are the common phenomena in most of the Asian countries (Autrup 2010 ). Besides, chemical and physical components like sea salt, biomass burning, Pb, road dust, black carbon, SO 2 , NO 2 , O 3 , CO are also responsible for PM 2.5 concentration (Rahman et al. 2019 ). Note that the concentration level of PM 2.5 and other metal substances in the air of the Dhaka and its adjacent areas are higher than Europe, East Asian, and other South Asian countries (Salam et al. 2008 ). Despite having some limitations, e.g., (i) a limited number of ground stations’ data for satellite data validation and (ii) insufficient sample sizes for a primary health data collection for both pregnant women and the 60 + population, this research identified (1) the high-pots zone of PM 2.5 concentrations, the urban areas, and (2) the most vulnerable population groups, the pregnant women and population 60 + . Therefore, the concerned authorities may consider this result in public health-related policy-making and /or modification regarding space and vulnerable population groups. Conclusions And Further Research In this research, the concentration of PM 2.5 during 2002–2019 and its impact on public health is mapped in the central part of Bangladesh, where both quantitative and qualitative measures are conducted. The results of this study can be summarized as followings: The concentration of PM 2.5 has increased by 42% during 2002–2019. The susceptible hotspot zones are located in the central part of the study area, the urban areas of the Dhaka, Narayanganj, Munshiganj, Narsingdi, and Gazipur Districts. The pregnant women and population 60 + are high sensible in terms of PM 2.5 both in the high- and low-spot zones. The pregnant women as the most vulnerable group but have less information about PM 2.5 than the population 60 + . Suppose the concentration of PM 2.5 and its high-spot zone increase, the vulnerability of public health along with all strata of the population will be affected over the next period. Moreover, overall urban ecology and morphology will be affected the most due to PM 2.5 . Note that this study may be useful for the government of Bangladesh, particularly for the Ministry of Environment and Climate Change and their urban and environment officials for designing an appropriate plan for local and regional air pollution mitigation. The Ministry of Health may consider the outcomes of this research to identify the most hotspot zones for establishing satellite and mobile health services. The methodology of the paper may be replicated to research other areas of Bangladesh. Future research is recommended based on (i) high-resolution (spatial and temporal) PM 2.5 of satellite data as it can be used as an independent data source for PM 2.5 concentrations where the ground-based stations’ data are time-consuming and expensive, and (ii) sufficient sample of primary health data from different respondents and/or communities. To overcome these limitations, future studies, including a wide range of scientific data, may be considered in a wider geographic area. Declarations Author contribution- Shareful: model conceptualization, methodology, data collection, analysis, writing the original draft. Tariqul: writing, review and editing. Amir: methodology, writing, review and editing, supervision. Funding- The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Data availability- All data generated or analysed during the current study are presented in this article. However, the raw data will be also accessible from the author group if requested. Ethics approval- We certify that this manuscript is original and has not been published and will not be submitted elsewhere for publication. This study follows all ethical practices during its writing. Consent to participate- All authors duly participated. 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Environmental Science and Pollution Research , 25(20), 20179–20192. https://doi.org/10.1007/s11356-018-1965-7 Zhao, J., Wang, X., Song, H., Du, Y., Cui, W., & Zhou, Y. (2019). Spatiotemporal trend analysis of PM2.5 concentration in China, 1999–2016. Atmosphere , 10(8), 1–10. https://doi.org/10.3390/atmos10080461 Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1605575","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":112957930,"identity":"cdfa4ecf-fb93-401e-8a6c-94ca215156a9","order_by":0,"name":"Shareful Hassan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACCRBhwyDDT6KWNAYeyQaStRgcIFaH/OzmZx8+JBzmMT6/Ok2CocYmmqAWgzvHjGfOAGoxu/F2swHDsbRcgg40kEgwZub9AdJyduMDxobDhLXIz0j/zPwH5LAZZzccIEoLw40cY2YGoBYD/l4ibTG4kVPM2JOQziNxg3ezQQIxfgE6bDPDjwRrOf7+s9skPtTYEOEwOJBIYGBIIF45CPAfIE39KBgFo2AUjBwAANf0P35PIBS1AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4668-2951","institution":"Jahangirnagar University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shareful","middleName":"","lastName":"Hassan","suffix":""},{"id":112957931,"identity":"89c57227-1ca9-47d3-99e3-ba3a618f357f","order_by":1,"name":"Md. Tariqul Islam","email":"","orcid":"","institution":"Bishop Grosseteste University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Tariqul","lastName":"Islam","suffix":""},{"id":112957932,"identity":"6bbb86f4-643e-4556-bfb7-4b02d9a435ea","order_by":2,"name":"Mohammad Amir Hossain Bhuiyan","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Amir Hossain","lastName":"Bhuiyan","suffix":""}],"badges":[],"createdAt":"2022-04-28 16:00:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1605575/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1605575/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":23601987,"identity":"c4b0cb9a-1ef8-461d-9686-c3b014a3e58d","added_by":"auto","created_at":"2022-07-07 18:59:02","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":360378,"visible":true,"origin":"","legend":"\u003cp\u003eThe location of the study area with ground stations’ place for PM\u003csub\u003e2.5\u003c/sub\u003e measurement, topography ( The National Aeronautics and Space Administration-NASA 2019), and population data (Bangladesh Bureau of Statistics-BBS 2020).\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/e98c531e1387ed45f858af7d.jpeg"},{"id":23602546,"identity":"9fba75ed-b92b-45ca-ad6c-0640869bcc6b","added_by":"auto","created_at":"2022-07-07 19:04:02","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23230,"visible":true,"origin":"","legend":"\u003cp\u003eA linear regression of estimated PM\u003csub\u003e2.5\u003c/sub\u003e using MODIS data in the x-axis (Sat) and ground monitoring data in the y-axis (CASE 2019).\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/ee29a3832fc5986a6887f164.jpeg"},{"id":23602654,"identity":"b8954691-398a-47cd-8ee4-7856a6f94439","added_by":"auto","created_at":"2022-07-07 19:09:02","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":572953,"visible":true,"origin":"","legend":"\u003cp\u003eA minimum, maximum, and mean value of PM\u003csub\u003e2.5 \u003c/sub\u003e(μg/m\u003csup\u003e3\u003c/sup\u003e) from 2002-2019 (a) and annual average PM\u003csub\u003e2.5 \u003c/sub\u003e(μg/m\u003csup\u003e3\u003c/sup\u003e) in the study area in a different year (b). District specific linear regression analysis is also presented here (c-h).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/b313d2de4a77b8cae4b4bcac.jpeg"},{"id":23601992,"identity":"b80f2b9c-a624-41d6-a79d-721327aeab11","added_by":"auto","created_at":"2022-07-07 18:59:02","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":513373,"visible":true,"origin":"","legend":"\u003cp\u003eThe average concentration of PM\u003csub\u003e2.5\u003c/sub\u003e\u003cstrong\u003e \u003c/strong\u003efrom 2002-2019.\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/fad20db8b17d8e320dc77681.jpeg"},{"id":23602548,"identity":"a6a64fc1-64ec-4e12-860c-e97445ea5f03","added_by":"auto","created_at":"2022-07-07 19:04:02","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":443858,"visible":true,"origin":"","legend":"\u003cp\u003eThe extracted (a) hotspot map was overlaid with (b) total population, (c) population age 0-9, (d) population age 60\u003csup\u003e+\u003c/sup\u003e, (e) pneumonia patients, and (f) pregnant women.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/b5e60be1e8ac5efedcb23a23.jpeg"},{"id":23601989,"identity":"4879baf5-5ee9-4312-ab5c-dd60bacca246","added_by":"auto","created_at":"2022-07-07 18:59:02","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57957,"visible":true,"origin":"","legend":"\u003cp\u003eRespondent’s knowledge about the sources of PM\u003csub\u003e2.5\u003c/sub\u003e in both high- and low-spot zones.\u0026nbsp;\u0026nbsp;\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/898d61bb12833ec2513563e2.jpeg"},{"id":23602655,"identity":"ded0bfb1-5d57-47dc-b392-52e021c25c81","added_by":"auto","created_at":"2022-07-07 19:09:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1050265,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1605575/v1/cf74dda5-b95e-4b72-870e-6a0187ffe980.pdf"}],"financialInterests":"","formattedTitle":"Assessment of Health Impacts of PM2.5 on the Vulnerable Groups in the Central part of Bangladesh","fulltext":[{"header":"Introduction","content":"\u003cp\u003eParticulate Matter (PM\u003csub\u003e2.5)\u003c/sub\u003e is one of the primary pollutants for ambient air pollution, which often imposes the greatest threat to public health, causing about 4.2\u0026nbsp;million global deaths a year (WHO \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Landrigan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The PM\u003csub\u003e2.5\u003c/sub\u003e (diameter\u0026thinsp;\u0026lt;\u0026thinsp;2.5 \u0026micro;m) has been exposed as the fundamental biological and environmental aspects by creating an adverse impact on regional and local public health (Autrup \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Long-term and short-term exposures to the high-level concentration of PM\u003csub\u003e2.5\u003c/sub\u003e are correlated with various public health problems, including death, respiratory difficulty, coronary disease, lung cancer, cardiac pain, asthma, and skin problem predominantly in urban and peri-urban areas (Andersen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hoek et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Raaschou-Nielsen et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Beelen et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dirgawati et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The most vulnerable population groups are children under aged five years, pregnant women, and the elderly (60\u003csup\u003e+\u003c/sup\u003e) who are sensitive to a high level of PM\u003csub\u003e2.5\u003c/sub\u003e due to many health issues (Luo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lei et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zeng et al. \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe PM\u003csub\u003e2.5\u003c/sub\u003e has been considered as one of the leading air pollutants in Dhaka and its adjacent areas, which has also been evidenced as an inevitable threat to human health as well as all living organisms (Kim et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Liang et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). It happens because of a large share of air (⁓58% of total PM\u003csub\u003e2.5\u003c/sub\u003e) of Dhaka and its adjacent areas are mixed by the toxic gasses mainly from brickfields operated in and around Dhaka (Begum et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, other reasons are motor vehicles (10.4%), road dust (7.70%), fugitive Pb (7.63%), soil dust (7.57%), biomass burning (7.37%), and sea salt (1.33%) (Begum et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In addition, the physical signature of PM\u003csub\u003e2.5\u003c/sub\u003e is being increased gradually because of unplanned rapid urbanization and swift industrialization to boost the country\u0026rsquo;s economy by generating the most unexchanged cost of environmental pollution (Zhang and Zhang 2018).\u003c/p\u003e \u003cp\u003eHowever, scrutiny through the PM\u003csub\u003e2.5\u003c/sub\u003e in Bangladesh, China, India, and Pakistan, it is found that around 86% of populations are exposed to the most extreme level (i.e., \u0026gt;\u0026thinsp;75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) of pollution concentration (HEI \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). If the daily concentration level of PM\u003csub\u003e2.5\u003c/sub\u003e increases by ten \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, the prevalence of respiratory and other health problems increases by 2.07%, while the hospital admission rate is increased by 8% (Dominici et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Zanobetti et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Xing et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to the World Bank report of 2018, every year, around seven million premature deaths occur worldwide due to PM\u003csub\u003e2.5\u003c/sub\u003e in which 234,000 deaths (3.34%) are recorded in Bangladesh (World Bank \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; PPI \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeographic Information System (GIS), together with remote sensing techniques, is a widely used method for Spatiotemporal hotspots analysis of PM\u003csub\u003e2.5\u003c/sub\u003e (Hoque et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This research aims to (i) perform a spatiotemporal mapping applying GIS using remotely sensed pixel-based time series PM\u003csub\u003e2.5\u003c/sub\u003e data considering a broader scale geographical context in Bangladesh (ii) measure the health effects of most vulnerable population groups such as pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e using primary health information, a self-reported stakeholders\u0026rsquo; perception contrasting very high and low concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e from hotspot areas, and (iii) test a hypothesis that pregnant women and the 60\u003csup\u003e+\u003c/sup\u003e population in the high-spot zones are more vulnerable than low-hot spot areas caused of the high concentration of PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e"},{"header":"Previous Studies On Pm And Its’ Impact On Public Health","content":"\u003cp\u003eGlobally, the remote sensing data coupled with primary health information have been used widely in air pollution-related health research to estimate the spatiotemporal pattern of PM\u003csub\u003e2.5\u003c/sub\u003e, the spatial extent of a hotspot, and impacts on public health, particularly on vulnerable groups. Hoque et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Cao et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) conducted such a study in the US, Europe, China, and India using Moderate Resolution Imaging Spectroradiometer (MODIS) data without validation with the ground or relevant data and linked the impact of PM\u003csub\u003e2.5\u003c/sub\u003e concentration and its exposure to different population groups. The validation process enhances the applicability of integrated MODIS and health data for the decision-making process. Knowing the spatial trend and geographical distribution of PM\u003csub\u003e2.5\u003c/sub\u003e is a vital policy aspect for the air pollution control mechanism. To understand this, Zhao et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) used a pixel-based liner pattern of PM\u003csub\u003e2.5\u003c/sub\u003e in China using 18 years MODIS data and energy consumption. But only energy consumption is not a major contributing factor to measure the increasing trend of PM\u003csub\u003e2.5,\u003c/sub\u003e which is a limitation.\u003c/p\u003e \u003cp\u003eSeveral visible and contributing factors from development and anthropogenic aspects may consider in understanding the real situation of an increasing trend. Hu et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) suggested that the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e depends on different geographic locations and land use classes. Using a Spatiotemporal Model, they found that the spatial trend of PM\u003csub\u003e2.5\u003c/sub\u003e concentration is more in urban areas than in rural or hilly areas (Hu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Used parameters in modeling were limited to five, along with PM\u003csub\u003e2.5\u003c/sub\u003e. However, running a robust model, different important variables, e.g., topographical, environmental, micro-climate, and anthropogenic, may generate more authentic results. Perception about air pollution, particularly on PM\u003csub\u003e2.5,\u003c/sub\u003e is a critical aspect in terms of public health research because people need to be aware of the adverse impact of PM\u003csub\u003e2.5\u003c/sub\u003e. Jiang et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) investigated public awareness on smog pollution in rural China and founded that the perception about air pollution at the individual level was much better. However, this study would have been more substantial if they could use children, pregnant women, and older people as critical respondents. Urban and urban slums are most vulnerable due to PM\u003csub\u003e2.5\u003c/sub\u003e within the urban context. Egondi et al. (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) conducted a cross-sectional study considering people ages 35\u003csup\u003e+\u003c/sup\u003e in Nairobi to collect and analyze the perception of air pollution for designing appropriate intervention strategies. The sample size being the representative of this study, it did not consider any parametric or non-parametric statistical analysis to compare their results with any control data, a limitation. However, Pithon (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggests that the control group is essential, and it resembles the impact between two groups with scientific evidence. Besides, Cao et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) show a robust correlation between PM\u003csub\u003e2.5\u003c/sub\u003e and mortality of lunch cancer in China, applying a statistical regression model using the old mortality data of 2008. Recent data of a variable can enhance the applicability of a regression model. Miller and Xu (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also suggest that panel or primary data are substantial to make a statistical relationship with PM\u003csub\u003e2.5\u003c/sub\u003e. Importantly, primary data can give real scenarios from the respondents, particularly from vulnerable groups.\u003c/p\u003e \u003cp\u003eIn the context of Bangladesh, a wide range of research for establishing a nexus between PM\u003csub\u003e2.5\u003c/sub\u003e and public health has been conducted in and around Dhaka city (Salam et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Begum et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Azkar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hoque et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Begum \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Rana et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Most of the studies were conducted in smaller geographic areas using handheld PM\u003csub\u003e2.5\u003c/sub\u003e sampling machines\u0026rsquo; data from only three ground stations, and the primary health data of vulnerable groups considering hotspot zones were ignored. However, to generalize the picture of the concentration of PM\u003csub\u003e2.5,\u003c/sub\u003e only three stations data, particularly in Dhaka Mega City in Bangladesh and its impact on health hazards, may be misleading. Therefore, in this study, a broader scale geographic area is selected considering MODIS data combine with peoples\u0026rsquo; perception of the vulnerable group that enhances the insight into a better understanding of PM\u003csub\u003e2.5\u003c/sub\u003e concentration and its impact on public health.\u003c/p\u003e"},{"header":"Study Area","content":"\u003cp\u003eThe study area of this research is located in the Dhaka Division of Bangladesh, covering its five central industrial Districts; Dhaka, Narayanganj, Munshiganj, Narsingdi, and Gazipur (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The entire geographic area lies between 23\u0026deg;20'N-24\u0026deg;20'N latitudes and 90\u0026deg;00'E-91\u0026deg;00'E longitudes, which covers about 6,043 square kilometers, including 22,066,710 populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Having a tropical wet monsoon and dry winter climate, the study area has an annual average rainfall of 1,854 mm with an average yearly temperature of 25 \u003csup\u003e0\u003c/sup\u003eC (Hossain and Bahauddin \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study area was selected due to some pragmatic reasons: (a) colossal population pressure, (b) massive industrial developments, (c) higher level of traffic concentration, (d) internal migration, and (e) unplanned urban products, which are the key controlling factors for its local and regional atmospheric conditions. Some scientists mentioned that this area has the largest density of industrialization due to easy access to finance, enormous transportation facilities, location-based advantage, spatial context, and different management services (Islam \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Many industries operate activities in the study area, which is the key triggering reasons to produce enormous emission and gaseous particulates (Salam et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). These industries include, e.g., ready-made garment, textile, pharmaceuticals, cement, brickfields, fertilizer, assembling of a motorcycle, bus, truck, Compress Natural Gas, raw material processing, food and sugar, and electrical power.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods And Results","content":"\u003cp\u003eThis study used the annual average data of PM\u003csub\u003e2.5\u003c/sub\u003e between 2002\u0026ndash;2016 and 2019 for the quantitative measures. The data for 2017 and 2018 are missing here. To understand the impact of PM\u003csub\u003e2.5\u003c/sub\u003e on public health, a structured questionnaire survey was conducted, a qualitative analysis. Details of the methods are described here.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRetrieving PM\u003csub\u003e2.5\u003c/sub\u003e data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe annual average data of PM\u003csub\u003e2.5\u003c/sub\u003e were collected as raster-ASCII format with a 0.01 X 0.01 deg spatial resolution from Van Donkelaar et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), a study group at the Atmospheric Composition Analysis Group of Dalhousie University, Canada. They derived the PM\u003csub\u003e2.5\u003c/sub\u003e from Aerosol Optical Depth (AOD) using the Moderate Resolution Imaging Spectroradiometer (MODIS), Multi-angle Imaging SpectroRadiometer (MISR), and SeaWiFS sensors. A robust Geographically Weighted Regression (GWR) method along with GEOS-Chem Models to simulate the spatiotemporal variations across the world, was applied (Van Donkelaar et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). The raster data were converted to point feature data within the study area using a district boundary (shapefile) as a mask applying open-source GIS software, QGIS 3.14 (QGIS \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) where each pixel generated one point feature. The shapefile (mask) was collected from Bangladesh Local Government and Engineering Department (LGED \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) with a coordinate reference system, World Geodetic System 1984 (WGS84). The converted point features were further used for geostatistical, hotspots, and risk zone analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003edata validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe PM\u003csub\u003e2.5\u003c/sub\u003e derived from satellite images were validated using ground stations\u0026rsquo; data during 2002\u0026ndash;2019 from the Department of Environment, Government of Bangladesh (CASE \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). There are only five ground stations (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e) available in the study area. The annual average MODIS and ground measured PM\u003csub\u003e2.5\u003c/sub\u003e data were used in a statistical correlation (Ni et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). The Correlation Coefficient (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e) and the Adjusted Correlation Coefficient were estimated (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The extracted PM\u003csub\u003e2.5\u003c/sub\u003e data from MODIS provided a good fit with the ground base measurement as \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e \u003csub\u003e=\u003c/sub\u003e 92.05% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). It revealed that the derived PM\u003csub\u003e2.5\u003c/sub\u003e data were estimated with high accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariable and Spatio-temporal analysis\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemporal analysis of basic statistics, e.g., minimum, maximum, and mean value of PM\u003csub\u003e2.5\u003c/sub\u003e during the study period 2002\u0026ndash;2019, was calculated. In this period, the mean annual rate of PM\u003csub\u003e2.5\u003c/sub\u003e is increased by ⁓42% in the study area (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea-c). The yearly trend of minimum values of PM\u003csub\u003e2.5\u003c/sub\u003e is increased by 40%, while the maximum value is increased by 37% (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). The concentration PM\u003csub\u003e2.5\u003c/sub\u003e is almost stable to 60\u0026thinsp;\u0026plusmn;\u0026thinsp;2 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e during 2003\u0026ndash;2008 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). The highest variation of PM\u003csub\u003e2.5\u003c/sub\u003e is 8%, found from 2012 to 2016 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb). Besides, an upward trend of the mean values is observed from 2013 to 2019 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eb), and the highest (Dhaka District) and lowest (Narsingdi District) increment happen with the gradient of 1.82 and 1.74, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ed, h). All these statistical values exceed the annual standard limit of the World Health Organization (WHO) that is 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003ea) (WHO \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eA time-series mapping was created using a specific year\u0026apos;s average values of PM\u003csub\u003e2.5\u003c/sub\u003e between 2002 and 2019 to visualize the spatiotemporal trend of PM\u003csub\u003e2.5\u003c/sub\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). However, to identify the most pollutant and affected zones in the study area, a general map was prepared using average value considering the entire study period (2002\u0026ndash;2019), the average map in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. In the Dhaka District, the average annual PM\u003csub\u003e2.5\u003c/sub\u003e is 65\u0026ndash;67 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e while it is 62\u0026ndash;65 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in Narayanganj, 60\u0026ndash;66 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in Gazipur, 61\u0026ndash;64 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e Narshingdi, and 63\u0026ndash;67 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in Munshiganj Districts (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The Dhaka District, the central part of the study area, has more signatures of air pollution than other parts. Predominantly, all urban cities of the middle part have higher concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e. On the other hand, the northern and southern parts of the study area have less pollution because of peri-urban and less industrial and brickfield activities (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHotspot analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial process of the statistical clustering method was considered to identify the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e pollutants in the long-term spatiotemporal pattern of air pollution (e.g., Habibi et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). In ArcsGIS, the Hot Spot Analysis tool in Spatial Statistics was applied to find out the hottest and coldest areas. In the hotspot analysis, Getis\u0026ndash;Ord Gi*cluster statistic method was selected as a local spatial statistic using average temporal vector data (point feature) of PM\u003csub\u003e2.5\u003c/sub\u003e. The Gi*cluster statistic works based on the weights and heterogeneity in each data point of PM\u003csub\u003e2.5\u003c/sub\u003e (Songchitruksa and Zeng \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). The Gi* statistic uses the following measures as mentioned by Environmental Systems Research Institute (ESRI) (ESRI \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) to identify the hotspots areas:\u003c/p\u003e\n\u003cdiv class=\"Equation\" id=\"Equa\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$${G}_{i}^{*}=\\frac{\\sum _{j=1}^{n}{w}_{i,j}{x}_{j}-\\stackrel{-}{X}\\sum _{j=1}^{n}{w}_{i,j}}{\\sqrt[s]{\\frac{\\left[n\\sum _{j=i}^{n}{w}_{i,j}^{2}-{\\left(\\sum _{j=1}^{n}{w}_{i,j}\\right)}^{2}\\right]}{n-1}}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003ewhere, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e is the value of \u003cem\u003ej, w\u003c/em\u003e\u003csub\u003e\u003cem\u003ei,j\u003c/em\u003e\u003c/sub\u003e is the spatial weight between feature \u003cem\u003ei\u003c/em\u003e and \u003cem\u003ej\u003c/em\u003e, \u003cem\u003en\u003c/em\u003e is equal to the number of features, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{X}= \\frac{\\sum _{j=1}^{n}{x}_{j}}{n}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(s= \\sqrt{\\frac{\\sum _{j=1}^{n}{x}_{j}^{2}}{n}-{\\left(\\stackrel{-}{X}\\right)}^{2}}\\)\u003c/span\u003e\u003c/span\u003e. A Getis\u0026ndash;Ord Gi* produces \u003cem\u003ez-\u003c/em\u003escores and \u003cem\u003ep-\u003c/em\u003evalue. A higher \u003cem\u003ez\u003c/em\u003e-score and a small \u003cem\u003ep\u003c/em\u003e-value of a cluster signify the hottest spot while a negative \u003cem\u003ez\u003c/em\u003e-score and a small \u003cem\u003ep\u003c/em\u003e-value present the coldest area (Jana and Sar \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e). Readers are encouraged to read (ESRI \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) more about \u003cem\u003ez\u003c/em\u003e-score and \u003cem\u003ep\u003c/em\u003e-value. The raster overlay was applied using the resultant hotspots areas and area-specific population and most critical public health data to find out the riskiest zones (e.g., Kumar et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). Further, the hotspot map was overlaid with upazlias\u0026rsquo; total population (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb), population 0\u0026ndash;9 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec), population 60\u003csup\u003e+\u003c/sup\u003e(Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed), pneumonia patient (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee), and pregnant women (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef). The upazilas\u0026rsquo; specific population data was collected from the Bangladesh Bureau of Statistics (BBS) (BBS \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) to calculate area-specific, most vulnerable population groups whose ages between 0\u0026ndash;9 and 60\u003csup\u003e+\u003c/sup\u003e years. The upazilas\u0026rsquo; specific pregnant and pneumonia patients were collected from Bangladesh Directorate General of Health Services (BDGHS) (DGHS \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eNear about one-third area is found as very high- and high-hotspot zones in the study (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). The spatial difference between the very high- and high-hotspots zones is almost negligible (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ea). Most of these very high-spot zones were found in all city areas of Dhaka, Gazipur \u003cem\u003esadar\u003c/em\u003e, Kaliganj, Rupganj, Sonargaon, Savar, and Dhamrai areas. Moreover, this research found 3640748 persons (16.5% out of total population) (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003eb) of which 4.39% (969261 persons) are age group 0\u0026ndash;9 (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ec), 0.95% (210999 persons) are age group 60\u003csup\u003e+\u003c/sup\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ed), 5% (12,062,419 persons) are pregnant women (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ee), and 1% (24621 persons) are pneumonia patients (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003ef) in very high- and high-hotspot zones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImpact of PM2.5 on public health\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe self-reported health impacts due to the PM\u003csub\u003e2.5\u003c/sub\u003e in the very high-, high-hotspots, and low-spots zones, a primary survey was conducted considering 115 sample populations. For this health survey, a purposive sampling method (Palinkas et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) was followed to collect the individual specific in-depth information from each respondent using a mini-structured questionnaire from 26 December 2019 to 27 January 2020. The health impacts between very high- and low-spot zones are compared. A total of 85 samples were conducted to the population age 60\u003csup\u003e+\u003c/sup\u003e, of which 55 samples were from very high- and 30 samples were from low-spot zones. On the other hand, 30 samples were conducted to the pregnant women, of which 20 samples were from very high- and ten samples were from low-spot zones. For collecting data about pregnant women, the survey team went to government health facilities and practice chambers of the gynecologist.\u003c/p\u003e\n\u003cp\u003eAfter getting a consensus from a pregnant woman or her caregivers, data was collected. For the population age 60\u003csup\u003e+\u003c/sup\u003e, respondents were selected as one in a ⁓5 km radius to enhance the uniform distribution of the sample. After data collection and editing, significant sources of air PM\u003csub\u003e2.5\u003c/sub\u003e and self-reported health impacts due to PM\u003csub\u003e2.5\u003c/sub\u003e were analyzed using descriptive analysis. A non-parametric Mann-Whitney U (McKnight and Julius \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) test to compare the health impacts between very high- and low-spot zones was conducted. This non-parametric test was selected because these two groups were not normally distributed, and the sample size was sufficiently small, what is one of the limitations of this study. However, this test tends to be more appropriate in this situation (McKnight and Julius \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Stata version 13 (StataCorp LLC n.d.) was used to conduct different statistical analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespondents\u0026rsquo; knowledge about PM\u003csub\u003e2.5\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbout 71% of the population age 60\u003csup\u003e+\u003c/sup\u003e in the high-spot zone know well about PM\u003csub\u003e2.5\u003c/sub\u003e (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Contrary, more than 73% of the same group (60+) do not know about PM\u003csub\u003e2.5\u003c/sub\u003e in the low-spot zone (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Only 55% of pregnant women in the high-spot area know about PM\u003csub\u003e2.5\u003c/sub\u003e, while 70% of pregnant women have no idea about PM\u003csub\u003e2.5\u003c/sub\u003e in the low-spot zone. In this analysis, pregnant women have less access to information related to PM\u003csub\u003e2.5\u003c/sub\u003e than the population 60\u003csup\u003e+\u003c/sup\u003e in both zones (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespondents\u0026rsquo; knowledge of sources of PM\u003csub\u003e2.5\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePopulation age 60\u003csup\u003e+\u003c/sup\u003e believe that road vehicle (64%), urbanization (84%), construction site (43%), brickfield (38%), and industrial emissions (44%) are responsible for PM\u003csub\u003e2.5\u003c/sub\u003e in high-spot zone. Contrary, pregnant women in the high-spot zone suggest that dust (25%), construction site (57%), and brickfield (%) are the predominant controlling factors for PM\u003csub\u003e2.5\u003c/sub\u003e (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). In the low-spot zone, both pregnant women and population age 60\u003csup\u003e+\u003c/sup\u003e mention that the dust (20%) and industrial emissions (56%) are the triggering factors for increasing PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRespondent\u0026rsquo;s perception about sources of PM\u003csub\u003e2.5\u003c/sub\u003e in both high- and low-spot zones.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePregnant women in low-spot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePregnant women in high-spot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e60\u0026thinsp;+\u0026thinsp;pop in low-spot\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e60\u0026thinsp;+\u0026thinsp;pop in high-spot\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoad vehicle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrbanization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.7%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrickfield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConstruction site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial emission\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRespondents\u0026rsquo; responses about health risk due to PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBreathing problems, cold/cough, eye problems, skin problems, and asthma are identified as significant health problems in both high- and low-spot zones. The Mann-Whitney U test results suggest that the pregnant women group has a higher mean rank (16.43) in the high-spot than the low-spot zone (13.65) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The Mann-Whitney U test is estimated to 81.5, and the p-value is to 0.422, which is higher than 0.05. So, the test is not statistically significant, but there the difference exists.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRespondent\u0026rsquo;s self-reported health risk due to particulate matter\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGroups\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eLow spot zone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHigh spot zone\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eMann-Whitney U\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean rank\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePregnant women\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003csup\u003e+\u003c/sup\u003e population\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec13\"\u003e\n \u003cp\u003e\u003cem\u003e* Statistically significant at 0.05\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eHowever, a higher mean rank of 48.26 for the population 60\u003csup\u003e+\u003c/sup\u003e is calculated in the high-spot, compared to that in the low-spot zone to 33.35. Besides, the Mann-Whitney U test is 535.5, and the p-value is 0.006., which is less than 0.05 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). It proves that there is a significant difference in the health impact of pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e between the high- and the low-spot zones. The health effects of pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e in high-spot regions are more significant than low-spot areas that satisfy the hypothesis.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussions","content":"\u003cp\u003eFor better understanding the effects of PM2.5 on pregnant women and 60\u003csup\u003e+\u003c/sup\u003e population health, this study is unique, particularly in the context of Bangladesh in many ways; e.g., (i) a large scale geographic area are considered what is usually ignored in many pieces of research (Begum et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Azkar et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Hoque et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Hu et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Rana et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Begum \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Rahman et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), (ii) remotely sensed PM\u003csub\u003e2.5\u003c/sub\u003e data is used that can be applied as an independent data source of PM\u003csub\u003e2.5\u003c/sub\u003e (Cao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zhao et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), however, it is further validated using ground-based stations\u0026rsquo; data, and (iii) qualitative measures by stakeholders\u0026rsquo; perceptions are combined with the quantitative measures that are very important in this particular issue (Egondi et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hoque et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A group of researchers conducted a validation of extracted PM\u003csub\u003e2.5\u003c/sub\u003e values from satellite images with ground station data, resulting in \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.54% in Beijing, China, which is less than this study (⁓0.92%) (Ni et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is because the Chinees research group has used a very scattered location of many ground stations\u0026rsquo; data (Ni et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Han et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found a very high \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e-value ranging from 0.72\u0026ndash;0.97% using 35 ground-based monitoring stations data. Therefore, it is recommended to use many and scattered locations of ground stations\u0026rsquo; data for statistical validation of satellite recorded PM\u003csub\u003e2.5\u003c/sub\u003e. However, validity estimation also depends on regional topography and weather patterns, e.g., relative humidity, atmospheric temperature, wind speed, and sessional climate variability (Al-Hamdan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Estimation of the spatiotemporal concentration of PM\u003csub\u003e2.5\u003c/sub\u003e is a critical issue for managing local and regional atmospheric pollution strategy as well as a public health concern. The average annual concentration of PM\u003csub\u003e2.5\u003c/sub\u003e is increased by 42% during 2002\u0026ndash;2019 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is because of excessive emissions of different kinds of diesel and petrol vehicles as well as poorly maintained automobiles, that are generating PM\u003csub\u003e2.5\u003c/sub\u003e pollutant in urban areas of Bangladesh (Begum \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Begum et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) suggest that the motor vehicles (10.4%), road dust (7.70%), fugitive Pb (7.63%), soil dust (7.57%), biomass burning (7.37%), and sea salt (1.33%) are responsible for PM\u003csub\u003e2.5\u003c/sub\u003e in the Dhaka city and its adjacent areas what is similarly responded by the stakeholders in this study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In Bangladesh and its megacities like Dhaka, ⁓35% of ambient PM\u003csub\u003e10\u003c/sub\u003e and ⁓15% of PM\u003csub\u003e2.5\u003c/sub\u003e are being generated from brick kiln emissions and transportation systems (Motalib and Lasco \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Comparable with China (60 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e), Bangladesh (77 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) generates a higher level of PM\u003csub\u003e2.5\u003c/sub\u003e in 2016 even though both countries have a similar pattern of population growth (Cao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Likewise in Bangladesh, ⁓88% of the areas in China had an increasing trend of PM\u003csub\u003e2.5\u003c/sub\u003e in the last 18 years due to huge traffic, transportation, and industrialization (Zhao et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), that is also similar in India (Kandlikar and Ramachandran \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). However, the dominant factors for increasing the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e in Vietnam are agriculture, cooking, heating, construction, and urbanization (Nguyen et al. 2018). However, the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e in the atmosphere depends on several anthropogenic factors such as transportation, industrial developments, and cooking and heating activities (Gautam et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Al-Hamdan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It also depends on some meteorological factors like wind speed, air relative humidity, cloud cover, and ambient temperature (Al-Hamdan et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). They suggest a large geographic area for investigation, and it is considered in this study. The results of this study reveal that the areas i.e. Dhaka, Narayanganj and Gazipur have more anthropogenic sources like manufacturing factories, high traffic congestion, and other combustion activities, ultimately leading to relatively a higher annual PM\u003csub\u003e2.5\u003c/sub\u003e concentration which is similar with the PM\u003csub\u003e2.5\u003c/sub\u003e concentration in the other developing countries like, China, India, Iran, and Tanzania (Mkoma et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tiwari et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Arfaeinia et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The other two study areas, the Narsingdi and Munshiganj Districts have a relatively low level of PM\u003csub\u003e2.5\u003c/sub\u003e concentration (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). The central part of the study area has been found in a higher concentration of PM\u003csub\u003e2.5\u003c/sub\u003e than the north and southern parts (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). However, incorporation of meteorological factors and seasonal variations could give more precise information about the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e fluctuation instead of just depending on annual average concentration which could often be misleading to describe the short-term anthropogenic activities or weather conditions, such as in Beijing\u0026ndash;Tianjin\u0026ndash;Hebei regions of China (Rajput et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Mangal et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). There are more than 1,850 ground-based air pollution monitoring stations in the European cities and among all, the sources of the maximum concentration of PM\u003csub\u003e2.5\u003c/sub\u003e of 12 cities are traffic-related (Kiesewetter et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), what is also similar in Bangladesh. Note that, the Saharan desert advection in the Mediterranean area (Ad\u0026atilde;es and Pires \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and the relative humidity with the traffic dust in Sacramento and California in the USA is the dominant factor for PM\u003csub\u003e2.5\u003c/sub\u003e (Mukherjee et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) what may not be comparable with PM\u003csub\u003e2.5\u003c/sub\u003e in this study.\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e is one of the significant public health concerns in urban and peri-urban areas of Bangladesh (Rahman et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The excessive standard threshold of PM\u003csub\u003e2.5\u003c/sub\u003e significantly impacts to vulnerable population groups, particularly pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e (Miller and Xu \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The higher concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and its adverse effects on the urban community is exposed as a common public health problem in Bangladesh (Begum et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Most of the public health problems are identified as pulmonary, cardiovascular, cancer, diabetics, chronic respiratory, low birth, weight, and premature death (Lawal and Asimiea \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Generally, pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e who are living in urban areas can be affected by the different respiratory and non-communicable diseases in many ways (Luo et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The significant health complexity of pregnant women and pneumonia patients is high in the high-spot zones (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-f ), which is similarly found in China (Andersen et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Arroyo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In Nanjing, China, the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e is responsible for high incidences of premature death (Li et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and in 2015, total premature death in China was recorded to 341,701 persons for stroke and 67,325 persons for lower respiratory infection linked to PM\u003csub\u003e2.5\u003c/sub\u003e concentration (Wang et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). By executing the hypothesis test, this research suggests that pregnant women\u0026rsquo;s health is more sensitive to the effects of PM\u003csub\u003e2.5\u003c/sub\u003e in the high-spot region than population 60\u003csup\u003e+\u003c/sup\u003e in the study area, which is similar in a developed country, like in Spain (Arroyo et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNevertheless, public perception of PM\u003csub\u003e2.5\u003c/sub\u003e is a critical issue, and a better understanding of the impact of PM\u003csub\u003e2.5\u003c/sub\u003e may reduce health burden in Asia and South Asia (Jiang et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cao et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Achakulwisut et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The pregnant women both in the high- (55%) and low-spot zone (30%) in this study area know little about the impact of PM\u003csub\u003e2.5\u003c/sub\u003e compared to population age 60\u003csup\u003e+\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Gender gaps in Bangladesh may cause it, similarly, found in the case of public information and awareness for pre-disaster preparedness for a tropical cyclone (R\u0026ouml;hr 2006). The stakeholders\u0026rsquo; perceptions in this study suggest that the awareness activities on PM\u003csub\u003e2.5\u003c/sub\u003e and air pollution happen more often in urban areas than in rural areas. Therefore, people are more informed about PM\u003csub\u003e2.5\u003c/sub\u003e in urban areas (high-spot zones) than the peri-urban area (low-spot zones). Besides, urban people have more means of getting air pollution-related information, e.g., high mobility, accessible information, mobile information desk, or portal than rural or peri-urban areas (Qian et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, the perception of air pollution depends not only on physical factors but also on employment, health, wages, place, and working environment (Achakulwisut et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe urbanization, road vehicles, brickfield and construction activities, and industrial emissions are the key controlling factors for increasing PM\u003csub\u003e2.5\u003c/sub\u003e in the study area. These are the common phenomena in most of the Asian countries (Autrup \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Besides, chemical and physical components like sea salt, biomass burning, Pb, road dust, black carbon, SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, CO are also responsible for PM\u003csub\u003e2.5\u003c/sub\u003e concentration (Rahman et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Note that the concentration level of PM\u003csub\u003e2.5\u003c/sub\u003e and other metal substances in the air of the Dhaka and its adjacent areas are higher than Europe, East Asian, and other South Asian countries (Salam et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite having some limitations, e.g., (i) a limited number of ground stations\u0026rsquo; data for satellite data validation and (ii) insufficient sample sizes for a primary health data collection for both pregnant women and the 60\u003csup\u003e+\u003c/sup\u003e population, this research identified (1) the high-pots zone of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations, the urban areas, and (2) the most vulnerable population groups, the pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e. Therefore, the concerned authorities may consider this result in public health-related policy-making and /or modification regarding space and vulnerable population groups.\u003c/p\u003e"},{"header":"Conclusions And Further Research","content":"\u003cp\u003eIn this research, the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e during 2002\u0026ndash;2019 and its impact on public health is mapped in the central part of Bangladesh, where both quantitative and qualitative measures are conducted. The results of this study can be summarized as followings:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe concentration of PM\u003csub\u003e2.5\u003c/sub\u003e has increased by 42% during 2002\u0026ndash;2019.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe susceptible hotspot zones are located in the central part of the study area, the urban areas of the Dhaka, Narayanganj, Munshiganj, Narsingdi, and Gazipur Districts.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe pregnant women and population 60\u003csup\u003e+\u003c/sup\u003e are high sensible in terms of PM\u003csub\u003e2.5\u003c/sub\u003e both in the high- and low-spot zones.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe pregnant women as the most vulnerable group but have less information about PM\u003csub\u003e2.5\u003c/sub\u003e than the population 60\u003csup\u003e+\u003c/sup\u003e.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eSuppose the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e and its high-spot zone increase, the vulnerability of public health along with all strata of the population will be affected over the next period. Moreover, overall urban ecology and morphology will be affected the most due to PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eNote that this study may be useful for the government of Bangladesh, particularly for the Ministry of Environment and Climate Change and their urban and environment officials for designing an appropriate plan for local and regional air pollution mitigation. The Ministry of Health may consider the outcomes of this research to identify the most hotspot zones for establishing satellite and mobile health services. The methodology of the paper may be replicated to research other areas of Bangladesh. Future research is recommended based on (i) high-resolution (spatial and temporal) PM\u003csub\u003e2.5\u003c/sub\u003e of satellite data as it can be used as an independent data source for PM\u003csub\u003e2.5\u003c/sub\u003e concentrations where the ground-based stations\u0026rsquo; data are time-consuming and expensive, and (ii) sufficient sample of primary health data from different respondents and/or communities. To overcome these limitations, future studies, including a wide range of scientific data, may be considered in a wider geographic area.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution-\u003c/strong\u003e \u003cstrong\u003eShareful:\u003c/strong\u003e model conceptualization, methodology, data collection, analysis, writing the original draft.\u0026nbsp;\u003cstrong\u003eTariqul:\u003c/strong\u003e writing, review and editing.\u0026nbsp;\u003cstrong\u003eAmir:\u003c/strong\u003e\u0026nbsp; methodology, writing, review and editing, supervision.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding-\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability-\u003c/strong\u003e All data generated or analysed during the current study are presented in this article. However, the raw data will be also accessible from the author group if requested.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval-\u003c/strong\u003e We certify that this manuscript is original and has not been published and will not be submitted elsewhere for publication. This study follows all ethical practices during its writing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate-\u003c/strong\u003e All authors duly participated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication-\u003c/strong\u003e This is confirmed that the publication of this manuscript has been approved by all co-authors.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests-\u003c/strong\u003e The authors declare no competing interests\u003c/p\u003e\n\u003cp\u003eBefore conducting the interview, each participant gave their informed consent. The participants in the study also consented to the publication of their information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAchakulwisut, P., Brauer, M., Hystad, P., \u0026amp; Anenberg, S. C. (2019). Global, national, and urban burdens of paediatric asthma incidence attributable to ambient NO 2 pollution: estimates from global datasets. \u003cem\u003eThe Lancet Planetary Health\u003c/em\u003e, 3(4), e166\u0026ndash;e178. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2542-5196(19)30046-4\u003c/span\u003e\u003cspan address=\"10.1016/S2542-5196(19)30046-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAd\u0026atilde;es, J., \u0026amp; Pires, J. C. M. (2019). 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Spatiotemporal trend analysis of PM2.5 concentration in China, 1999\u0026ndash;2016. \u003cem\u003eAtmosphere\u003c/em\u003e, 10(8), 1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/atmos10080461\u003c/span\u003e\u003cspan address=\"10.3390/atmos10080461\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bangladesh, Air pollution in Dhaka, GIS, MODIS recorded PM2.5, Spatiotemporal PM2.5, Geostatistical analysis","lastPublishedDoi":"10.21203/rs.3.rs-1605575/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1605575/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParticulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) is one of the critical sources for outdoor air pollution and poses the most significant public health threat. In Bangladesh, particularly in the major urban cities, PM\u003csub\u003e2.5\u003c/sub\u003e has been identified as a significant public health hazard. This research aims to perform a spatiotemporal mapping of PM\u003csub\u003e2.5\u003c/sub\u003e from 2002\u0026ndash;2019 to identify the hotspots in central Bangladesh and to estimate the health impacts on pregnant women and population aged 60\u003csup\u003e+\u003c/sup\u003e. A time-series of remotely sensed PM\u003csub\u003e2.5\u003c/sub\u003e is used in hotspot analysis, applying Geographic Information Systems (GIS). To explore the health impacts due to PM\u003csub\u003e2.5\u003c/sub\u003e, a questionnaire survey is conducted on pregnant women and population aged 60\u0026thinsp;+\u0026thinsp;in both high- and low-spots zones. A non-parametric statistical analysis is conducted to understand the mean impacts. The findings of this research reveal that the annual concentration of PM\u003csub\u003e2.5\u003c/sub\u003e is increased by 47% during 2002\u0026ndash;2019. Most of the high hotspot zones are identified in the middle of the study areas; the core urban areas of Dhaka, Narayanganj, and Gazipur Districts. The traffic vehicles, urbanization, construction and brickfield activities, and industrial emissions are the main controlling factors for increasing PM\u003csub\u003e2.5\u003c/sub\u003e. Further, the health impacts of both pregnant and population 60\u003csup\u003e+\u003c/sup\u003e are higher in the high-spot zone than in the low-spot area. Note that pregnant women have less PM\u003csub\u003e2.5\u003c/sub\u003e related information than 60\u003csup\u003e+\u003c/sup\u003e population in both high- and low-spot zones. On the policy applications, the relevant departments may utilize these findings for health hazard risk reduction and local and regional air pollution mitigation.\u003c/p\u003e","manuscriptTitle":"Assessment of Health Impacts of PM2.5 on the Vulnerable Groups in the Central part of Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-07-07 18:59:00","doi":"10.21203/rs.3.rs-1605575/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f58090b1-e205-42e8-ae54-c8f4ee7100b1","owner":[],"postedDate":"July 7th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-07-07T18:59:00+00:00","versionOfRecord":[],"versionCreatedAt":"2022-07-07 18:59:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1605575","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1605575","identity":"rs-1605575","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}