Biomass burning refers to the combustion of organic matter, such as wood or crop waste, which can either be burned directly or converted into gaseous or liquid fuels. It is a widespread phenomenon driven by both natural processes and human activities, playing a crucial role in shaping air quality on regional and global scales. Although biomass burning has long been an agricultural practice, the increasing population and demand for agricultural land and food have significantly influenced its extent. Globally, farmers use fire as a tool for removing agricultural waste and excess crop residue from fields. Agricultural biomass burning refers to the intentional burning of crop residues, such as stubble, straw, or other agricultural waste, as a method to clear fields for the next planting season or for disposal purposes. Though Bangladesh is a fast-developing nation in Southeast Asia, the majority of its population about 80% still lives in rural regions. The biomass produced by forestry and agriculture is a significant source of fiber, food, fuel, fodder, and organic fertilizer in rural parts of developing nations. Thus, there is pressure from several sectors on biomass.
Bangladesh is predominantly an agricultural nation, thus burning agricultural waste is a popular practice here, especially during the dry season, which runs from October to March. Significant amounts of pollutants can be produced and released into the atmosphere by biomass burning, which includes peatland fires, wildfires, open-burning agriculture, household biofuel combustion, forest fires, and grass fires. With effects on public health (
Johnston et al., 2014
;
Linares et al., 2015
;
Yao et al., 2016
), air quality (
Jacobson, 2014
), and climate (
Jacobson, 2014
), open and agricultural fires are one of the major contributors to primary emissions to the atmosphere. Due to the short duration of these emissions a few weeks to months—they pose a considerable danger to human health as well as have a significant impact on air quality (
Koe et al., 2001
;
Pöschl, 2005
;
Gustafsson et al., 2009
;
Choudhury & Dey, 2017
).
As biomass burning is a prevalent anthropogenic activity, it significantly contributes to air pollution, posing substantial environmental and public health challenges globally. However, the smoke emissions from these agricultural fires have an immense impact, especially in places like East Asia where burning seems to be very widespread (
Streets et al., 2003
). These can have a major impact on the ecosystem locally as well as far downwind; they can even have an impact on highly inhabited metropolitan regions that are located far from the agricultural areas itself (
Chan & Yao, 2008
;
Zhang et al., 2011
). Increases in concentrations of O3 (
Brook et al., 2002
), CO (
Bell et al., 2009
), NO2 (
Latza et al., 2009
), and particulate matter have been associated with detrimental health consequences and increased mortality (
Garrett & Casimiro, 2011
;
Di et al., 2017
). The burning of biomass releases gases and particulates that exacerbate respiratory ailments such as lung infections, asthma, and chronic obstructive pulmonary disease (
Johnston et al., 2014
;
Yao et al., 2016
). The tiny particles, particularly PM2.5, have the greatest chance to harm health since they can penetrate the lungs deeply. There is continuous evidence indicating children and adults who are exposed to biomass smoke have a higher chance of developing common and severe illnesses. Particulate matter is thought to be responsible for around 3% and 5% of deaths from lung cancer and cardiovascular illnesses, respectively (
WHO, 2013
). Given that black carbon emissions may play a substantial role in aerosol concentrations, it is critical to assess the effects of agricultural and open fires on air pollution levels and the health consequences that follow.
According to
Kim et al. (2015)
, there is strong evidence linking exposure to air pollution to adverse health outcomes, including cardiovascular and respiratory disorders, as well as reduced lung function. Agricultural biomass fire smoke exposure can lead to respiratory disorders, optical irritation, allergic responses, and other health issues, especially in susceptible groups including children, the elderly, and those with previous health issues. Biomass burning may have negative economic effects on nearby towns, farmers, and agriculture-related industries. Analysing these effects aids in locating prospects for diversifying sources of income and sustainable alternatives.
This research aims to enhance the understanding of this pressing environmental issue and provide insights for improved mitigation and management strategies by analysing the complex interactions between biomass burning and air pollution. Efforts are being made to explore alternative waste management techniques—such as composting or utilizing crop residues for bioenergy production—to reduce the harmful environmental impacts of agricultural biomass burning. This study seeks to assess the current state of pollution caused by biomass burning in rural areas and its effects on public health. It is anticipated that the findings will contribute to sustainable biomass management practices and raise awareness about the pollution associated with biomass burning. Given the significant impact of biomass burning on climate and air quality, ongoing research continues to investigate its emissions and their broader environmental consequences.
2. Methods2.1. Nature of the Study
This study investigates the effects of biomass burning on air quality and health impacts in Madargonj Upazila, Jamalpur, Bangladesh. The study employs quantitative methods, utilizing air quality monitoring, spatial analysis, and health risk estimation. Using an Aeroqual Handheld Air Quality Monitor series 500, the study monitors air contaminants at seven monitoring stations.
2.2. Study Samples
A total of 7 air quality monitoring stations were systematically selected representing diverse biomass burning activities. While the limited number of stations provides a snapshot of pollution levels, it is acknowledged that this sample size may limit generalizability. Future studies are recommended to expand sampling across seasons and larger geographical areas to capture temporal and spatial variability. Data was gathered from December 22, 2024 to December 26, 2024 the selected seven distinct stations. These 7 sample sites were selected in a systematic manner to complete the study efficiently. Data were collected from Shamgonj Kalibari, Nandina, Baromaisha, Krishnopur, Milon Bazar, Jurkhali, Chor Bhatiani at Madargonj upazila in Jamalpur district, Bangladesh.
The selection of seven monitoring stations was based on 1) the homogeneity of biomass burning practices across Madargonj Upazila, 2) logistical feasibility given resource constraints, and 3) the need for spatially distributed data to capture localized pollution hotspots. While this sample size limits extrapolation to larger regions, it provides a robust baseline for future studies in similar rural agricultural contexts.
2.3. Parameters of Interest
The study monitored the following air pollutants:
The Aeroqual Handheld Air Quality Monitor (Series 500) was utilized for real-time measurement (
Figure 1
). To improve data reliability, future studies could incorporate additional validation methods, such as cross-referencing with stationary air quality monitors or secondary datasets.
The portable monitors can be configured with 28 different gas sensors (CH4, PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2, CO2, VOC) and particulate matter. It is possible to measure the target gases in ambient air at different concentrations in outdoor and indoor environments.
2.4. Data Analysis
A GPS device was used to record the longitude and latitude in the field, and the data was subsequently entered into ArcGIS 10.8.2, a geographic information system application. After data collection, Microsoft Word 2007 and Microsoft Excel 2007 were used to organize and evaluate the data. The Aeroqual Series 500 is used to collect the data from the machine, and it is then used to present the data
<xref ref-type="bibr" rid="scirp.144072-"></xref>Figure 1. Aeroqual handled air quality monitor with sensors. (PM<sub>2.5</sub>, PM<sub>10</sub>, CH<sub>4</sub>, CO, Cl<sub>2</sub>, NH<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO<sub>2</sub>).
using simple statistical approaches and methodologies, including a variety of tables, diagrams, graphs, and flow charts, among other things. Data on (CH4, PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2, CO2) has been collected from the device. The facts obtained for the research paper’s major portion are explained using tables and graphs, which are also used to highlight the theme of the study.
Inverse Distance Weighted (IDW) interpolation was selected for its simplicity and effectiveness in visualizing spatial trends with limited monitoring points. However, IDW assumes uniform spatial dependence, which may not fully capture micro-scale variability. Future work could validate these results with kriging or land-use regression models.
<xref ref-type="bibr" rid="scirp.144072-"></xref>2.5. The Spatial Distribution Method for Showing (CH<sub>4</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, CH<sub>4</sub>, CO, Cl<sub>2</sub>, NH<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO<sub>2</sub>)
ArcGIS 10.8.2 was used to analyze the geographical distribution of particular air pollutants in Madargonj Upazila’s biomass burning pollution. GIS-based pollution mapping makes use of interpolation techniques. The interpolation method employed in the study was Inverse Distance Weighted (IDW). The IDW interpolation approach is a multivariate interpolation analysis that uses a known set of scattered points to identify unknown points. The weighted average of the known points is used to identify the data for the unknown points. The weights, which are a function of the distance between the sampled and unsampled locations, can be calculated using the IDW power coefficient.
The IDW-derived spatial distributions should be interpreted with caution, as the small number of stations may oversimplify pollution gradients. Ground-truthing with additional sensors or hybrid modeling approaches would improve accuracy.
2.6. Methods to Identify the Health Impacts on People
We use survey unit-level and regional-level pollution data to evaluate the extent of exposure to the following pollutants: (CH4, PM2.5, PM10, CO, Cl2, NH3, SO2, NO2, CO2). Next, by assigning to each survey unit’s occupants a distance-weighted average of pollutant concentrations (CH4, PM2.5, PM10, CO, Cl2, NH3, SO2, NO2, CO2) predicted by the Ostro (1994), we utilize the concentration response to evaluate the health effects of this exposure. In this investigation, the following air pollutants are the focus: PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2, CO2. Particles containing a mixture of solid and liquid droplets exist in the atmosphere for a longer period of time than larger particles due to their smaller size and light weight. This is small enough to be breathed and enter the respiratory system profoundly after being absorbed into the bloodstream.
Health impact estimates were derived from pollutant exposure levels but do not account for individual-level confounders (e.g., pre-existing respiratory conditions, access to healthcare, or concurrent exposure to indoor air pollution). These factors may modify risk and should be investigated in future longitudinal studies.
All people are not exposed to equal concentration. However, three categories of concentration may be taken such as
1) Average exposure concentration.
2) Moderate exposure concentration.
3) High exposure concentration.
According to the World Bank, WHO, the average exposure concentration and value is 122.7 µg/m3. They proposed that the population who are overexposed should be exposed three times.
1) Average exposure concentration 122.7 µg/m3.
2) Moderate exposure concentration 245.4 µg/m3.
3) High exposure concentration 368.4 µg/m3.
The “URB AIR” urban air quality management plan in Asia, greater Mumbai report, provides a dose-response relationship that can be used to estimate the excess fatality caused by exposure to pollutants (CO2, PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2).
A World Bank publication, as indicated below,
Excess death = 0.00112 × [Pollutants − 41] × P × C
where,
P = number of people exposed
C = Crude mortality rate 0.00535 (according to World Atlas Data, 2015)
High exposure = 0.00112 × [368.4 − 41] × P × C
Moderate exposure = 0.00112 [245.4 − 41] × P × C
Average exposure = 0.00112 × [122.7 − 41] × P × C
Excess death = 0.00112 × [Pollutants − 41] × P × C
where,
P = number of people exposed
C = Crude mortality rate 0.00756 (according to world Atlas Data, 2015)
High exposure = 0.00112 × [368.4 − 41] × P × C
Moderate exposure = 0.0012 × [245.4 − 41] × P × C
Average exposure = 0.00112 × [122.7 − 41] × P × C
a) Bronchitis
Change in yearly cases of chronic bronchitis per 100,000 people is estimated at 6.12 pollutants (CO2, CH4, CO, Cl2, NH3, SO2, NO2).
The number of cases = 6.12 × [Pollutants − 41] < P
High exposure = 6.12 × [368.4 − 41] × P
Moderate exposure = 6.12 × [245.4 − 41] × P
Average exposure = 6.12 × [122.7 − 41] × P
b) Restricted Activity Days
Change in yearly cases of restricted activity days per person estimate pollutants. If the WHO standards are used, the change = 0.0575 × [Pollutants − 41] × P
High exposure = 0.0575 × [368.4 − 41] × P
Moderate exposure = 0.0575 × [245.4 − 41] × P
Average exposure = 0.0575 × [122.7 − 41] × P
c) Respiratory Hospital Diseases
Change respiratory hospital diseases per 100,000 people estimated at 1.2 using WHO standard.
Respiratory hospital diseases per 100,000 are estimated at 1.2 × [Pollutants − 41] × P/100,000
Change Emergency room visits per 10,000 people estimated at 23.54 µg/m3 PM using WHO standard, Emergency room visit per 100,000 are estimated at 23.54 [pollutants – 41] P/100,000
Average exposure = 23.54 × [122.7 − 41] × P/10,000
e) Asthma Attack
Change in daily asthma attack per cases of Asthmatic person is estimated.
The number of asthmatic persons is estimated at 7% of the population.
The number of asthma attack = 0.0326 × [pollutants − 41]
High exposure = 0.0326 × [368.4 − 41]
Moderate exposure = 0.0326 [245.5 – 41]
Average exposure = 0.0326 × [122.7 − 41]
f) Respiratory Symptom Days
Respiratory symptom days per person estimated per year are estimated at 0.183 × [pollutants − 41] < h
High exposure = 0.183 × [368.4 − 41] × h
Moderate exposure = 0.183 × [245.4 − 41] × h
Average exposure = 0.183 × [122.7 − 41] × h
3. Result3.1. Types and Amount (kg) of Biomass Used in the Study Area, 2023
The quality, quantity and kind of biomass used for burning in agricultural land are significant as they have varying energy content, combustion characteristics, and environmental and health impacts. Different types of biomasses such as water hyacinth, grass and weeds, dried leaves are mainly used for burning in the study area. Here, we indicate the sampling locations Shamgonj Kalibari, Milon Bazar, Jorkhali, Nandina, Baromaisha, Chor Bhatiani, Krisnopur respectively with the number of 1, 2, 3, 4, 5, 6, 7.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Table 1. Types and amount (kg) of biomass used in the study area, 2023.
No of sampling location
Using type of biomass used in the study area
Amount of biomass used (kg) in the study area
Percentage of biomass uses as amount
1, 2, 3, 4, 5, 6, 7
Water hyacinth
1680
60%
2, 3, 5, 7
Grass and weeds
750
26%
1, 4, 6
Dried leaves
400
14%
(Source: Field survey, 2023).
As shown in “
Table 1
” among the different biomass sources, water hyacinth was the most commonly used, accounting for 60% of the total biomass consumption, with 1680 kg utilized across sampling locations 1, 2, 3, 4, 5, 6, and 7. Grass and weeds were the second most used biomass type, comprising 26% of the total, used mainly in locations 2, 3, 5, and 7, with a total consumption of 750 kg. The least used biomass was dried leaves, contributing 14% (400 kg) and utilized in locations 1, 4, and 6. This distribution reflects the availability and preference of local communities for specific biomass resources in their daily activities.
As shown in “
Figure 2
” this distribution is also visually represented by the percentage of different biomass types used, reflecting the availability and preference of local communities for specific biomass resources in their daily activities. It is clearly visualized the dominance of water hyacinth in the study area.
Figure 2. Percentage of different types of biomasses used in the study area, 2023.
3.2. The Spatial Distribution Method for Showing (CH<sub>4</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, CO, Cl<sub>2</sub>, NH<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO<sub>2</sub>)
As shown in
Table 2
the concentration of CH4 ranges from 76 - 230 mg/m3 with the mean value of 139.36 mg/m3 (212. 96 ppm). The mean concentration of CH4 in this study is found higher than the study of Gazipur City ranging from 0 to 4.66 mg/m3 (
Rehnuma et al., 2023
), a combined study of Dhaka, Narayanganj, Munshiganj, Narsingdi and Gazipur ranging from 0.63 - 0.67 ppb (
Hassan & Bhuiyan, 2021
), Chittagong City with the mean value of 4.85 ppm (
Rouf et al., 2012
), and Shanghai, China 2.154 ppm (
Wei et al., 2020
). However, the spatial distribution (
Figure 3
) showed the highest concentration of CH4 in Shamgonj Kalibari which is about 228.98 mg/m3 and the lowest concentration is 76 mg/m3 which was found at Milon Bazar.
As shown in
Table 2
, the concentration of SO2 ranges from 4.2 - 13 mg/m3 with the mean value of 7.05 mg/m3 (2.69 ppm). The mean concentration of SO2 in this study was found higher than the study of Nanjing, China (0.005 ppm) (
Hasnain et al., 2023
) Shijiazhuang, China (0.008 ppm) (
Tui et al., 2021
), Delhi, India (0.002 ppm) (
Tyagi et al., 2016
), Narayanganj, Bangladesh (0.006 ppm) (
Rahman et al., 2019
), Chittagong, Bangladesh (0.0032 - 0.0128 ppm) (
Hoque et al., 2022
), and Dhaka (0.0102 - 0.0234 ppm) (
Hoque et al., 2020
). However, the spatial distribution (
Figure 4
) showed the highest concentration of SO2 in Jorkhali which is about 12.6 mg/m3 and the lowest concentration is 4.2 mg/m3 which was found at Shamgonj Kalibari.
As shown in
Table 2
the concentration of NO2 ranges from 0.087 - 0.095 mg/m3 with the mean concentration of 0.092 mg/m3 (0.049 ppm). The mean concentration
Figure 3. Spatial distribution of CH<sub>4</sub> concentration in the study area.
Figure 4. Spatial distribution of SO<sub>2</sub> concentration in the study area.
of NO2 in this study was found higher than the study of Nanjing, China (0.03 ppm) (
Hasnain et al., 2023
) Shijiazhuang, China (0.02 ppm) (
Tui et al., 2021
), Narayanganj, Bangladesh (0.01 ppm) (
Rahman et al., 2019
), Chittagong, Bangladesh (0.024 - 0.065 ppm) (
Hoque et al., 2022
), Dhaka (0.016 - 0.055 ppm) (
Hoque et al., 2020
) and Barapukuria (0.034 mg/m3) and lower than the study of Delhi, India (0.400 ppm) (
Tyagi et al., 2016
). However, the spatial distribution (
Figure 5
) showed the highest concentration at Chor Bhatiani which is about 0.095 mg/m3 and the lowest concentration is 0.087 mg/m3 which was found at Milon Bazar.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Figure 5. Spatial distribution of NO<sub>2</sub> concentration in the study area.
As shown in
Table 2
the concentration of CO2 ranges from 1185.58 - 1658.22 mg/m3 where the average value is 1392.54 mg/m3 (773.81 ppm). The mean concentration of CO2 in this study was found higher than the study of Shanghai, China (428.36 pm) (
Wei et al., 2020
), Dhaka (427 ppm) (
Pavel et al., 2020
) and
Rehnuma, Riad, and Shakur (2020)
observed the average concentration of CO2 ranged from 920 to 1238 mg/m3 from different locations of Tangail Sadar Upazila which was mostly similar range to the findings to this present study and also with the similar range of Gazipur City ranging from 1103 to 1507.67 mg/m3 (
Rehnuma et al., 2023
) and lower than the study of Jessore, Bangladesh (1061 - 2459 mg/m3) (
Akteruzzaman et al., 2023
) and Rajshahi (1020 ppm) (
Mahmud, Bari, & Rahman, 2017
). However, the spatial distribution (
Figure 6
) showed the highest concentration of CO2 in Krisnopur which is about 1658.22 mg/m3 and the lowest concentration is 1185.6 mg/m3 which was found at Chor Bhatiani.
Figure 6. Spatial distribution of CO<sub>2</sub> concentration in the study area.
As shown in
Table 2
the concentration of Cl₂ ranges from (0 - 0.54) mg/m3 where the average value is 0.11 mg/m3. However, the spatial distribution (
Figure 7
) showed the highest concentration of Cl₂ in Baromaisha, which is about 0.54 mg/m3 and the lowest concentration is 0 mg/m3 was found at Shamgonj Kalibari and Milon Bazar. Hydroxyl and ozone are the most abundant tropospheric oxidants, but chlorine atoms are much more reactive and can oxidize functional groups or whole molecules that are resistant to the weaker common oxidants. This can increase the formation of harmful secondary pollutants including particulate matter (PM) and ozone (
Ediagbonya & Tobin, 2020
).
As shown in
Table 2
the concentration of NH3 ranges from (0.067 - 0.28) mg/m3 where the average value is 0.217 mg/m3.
Rehnuma et al. (2020)
observed the average concentration of NH3 ranged from 0.06 - 0.30 mg/m3 from different locations of Gazipur City which was mostly similar range to the findings to this present study and
Kawashima et al. (2022)
also observed NH3 in Dhaka city where average concentration of NH3 was found accounting 40.8 µg/m3 (3.0 - 154.6 µg/m3). However, the spatial distribution (
Figure 8
) showed the highest concentration of NH3 in Chor Bhatiani which is about 0.277 mg/m3 and the lowest concentration is 0.067 mg/m3 which was found at Nandina.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Figure 7. Spatial distribution of Cl<sub>2</sub> concentration in the study area.
Figure 8. Spatial distribution of NH<sub>3</sub> concentration in the study area.
As shown in
Table 2
the concentration of CO ranges from (0.47 - 0.5) mg/m3 where the mean concentration is 0.490 mg/m3 (0.428 ppm). The mean concentration of CO in this study was found lower than the study of Nanjing, China (0.89 ppm) (
Hasnain et al., 2023
) Shijiazhuang, China (1.21 ppm) (
Tui et al., 2021
), Delhi, India (2.3 ppm) (
Tyagi et al., 2016
), Narayanganj, Bangladesh (1.4 ppm) (
Rahman et al., 2019
), Chittagong, Bangladesh (0.6 - 1.2 ppm) (
Hoque et al., 2022
), and higher than the study of Dhaka (0.0012 - 0.0037 ppm) (
Hoque et al., 2020
), Barapukuria (0.0093 mg/m3) and with the similar range of Serbia (0.69 ppm) (
Davidovic et al., 2021
). However, the spatial distribution (
Figure 9
) showed the highest concentration of CO in Shamgonj kalibari, Milonbazar, Nandina, Baromaisha, Chor Bhatiyani which is about 0.5 mg/m3 and the lowest concentration is 0.47 mg/m3 which was found at Krisnopur and Jorkhali.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Figure 9. Spatial distribution of CO concentration in the study area.
As shown in
Table 2
the concentration of ranges PM2.5 from (0.65 - 1.3) mg/m3 with the mean value of 0.880 mg/m3 (880 µg/m3). The mean concentration of PM2.5 in this study was found higher than the study of Nanjing, China (65.36 µg/m3) (
Hasnain et al., 2023
) Shijiazhuang, China (70.64 µg/m3) (
Tui et al., 2021
), Delhi, India (134 µg/m3) (
Tyagi et al., 2016
), Narayanganj, Bangladesh (203.2 µg/m3) (
Rahman et al., 2019
), Serbia (100 µg/m3) (
Davidovic et al., 2021
), Chittagong, Bangladesh (14.6 - 93.5 µg/m3) (
Hoque et al., 2022
), Dhaka (39.65 - 125.66 µg/m3) (
Hoque et al., 2020
), Jessore, Bangladesh (20.63 - 23.72 µg/m3) (
Akteruzzaman et al., 2023
), Gazipur (37.5 - 208 µg/m3) (
Mukta et al., 2020
) and Chittagong City with the mean value of 327 µg/m3 (
Rouf et al., 2012
). However, the spatial distribution (
Figure 10
) showed the highest concentration of PM2.5 was at Jorkhali, which is about 1.256 mg/m3 and the lowest concentration was 0.65 mg/m3 which was found at Baromaisha.
Figure 10. Spatial distribution of PM<sub>2.5</sub> concentration in the study area.
The concentration of PM10 ranges from (0.72 - 1.5) mg/m3 where the mean value is 1.14 mg/m3 (1140 µg/m3). The mean concentration of PM10 in this study was found higher than the study of Gazipur (85.6 - 300 µg/m3) (
Mukta et al., 2020
), Dhaka 139.1 - 195.3 µg/m3) (
Rahman et al., 2019
), Nanjing, China (102.75 µg/m3) (
Hasnain et al., 2023
), Shijiazhuang, China (132.77 µg/m3) (
Tui et al., 2021
), Delhi, India (378 µg/m3) (
Tyagi et al., 2016
), Narayanganj, Bangladesh (358 µg/m3) (
Rahman et al., 2019
), Malaysia (65.86 µg/m3) (
Mohtar et al., 2018
), Serbia (250 µg/m3) (
Davidovic et al., 2021
), Chittagong, Bangladesh (26.9 - 210 µg/m3) (
Hoque et al., 2022
), Dhaka (76.5 - 219 µg/m3) (
Hoque et al., 2020
), Dhaka (87.1 µg/m3) (
Pavel et al., 2020
), and Chittagong City with the mean value of 545 µ µg/m3 (
Rouf et al., 2012
). However, the spatial distribution (
Figure 11
) showed the highest concentration of PM10 was found at Jorkhali, which is about 1.457 mg/m3 and the lowest concentration was 0.721 mg/m3 which was found at Shamgonj Kalibari.
Figure 11. Spatial distribution of PM<sub>10</sub> concentration in the study area.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Table 2. Pollutants concentration in the study area.
Pollutants concentration in the study area (mg/m3)
Parameter name
Time
Location
min avg
Max avg
Mean
Mean (ppm)
Shaymgonj kalibari
Milon Bazar
Jorkhali
Nandina
Baromaisha
Chor Bhatiyani
Krishnopur
CO2
Morning
1185
1490
1206
1133
1217
1140
1520
1133
1520
1392.5
773.81
Noon
1700
1521
1542
1175
1619
1235
1732.66
1175
1732.66
Evening
1322
1374
1616
1290
1322
1181.74
1722
1181.74
1722
Average
1402.33
1461.67
1454.67
1199.33
1386
1185.58
1658.22
1185.58
1658.22
CO
Morning
0.5
0.5
o.47
0.5
0.5
0.5
0.47
0.47
0.5
0.49
0.428
Noon
0.5
0.5
0.47
0.5
0.5
0.5
0.47
0.47
0.5
Evening
0.5
0.5
0.47
0.5
0.5
0.5
0.47
0.47
0.5
Average
0.5
0.5
0.47
0.5
0.5
0.5
0.47
0.49
0.49
SO2
Morning
0.8
3.6
2.9
0.9
0.8
4.5
5.9
0.47
4.5
7.05
2.69
Noon
6.2
9.5
13.1
8.7
8.5
8.4
11.6
6.2
13.1
Evening
5.6
5.5
21.8
7.2
7.2
7.2
8.3
5.5
21.8
Average
4.2
6.2
12.6
5.6
5.5
6.7
8.6
4.2
12.6
NO2
Morning
0.091
0.086
0.089
0.091
0.09
0.092
0.091
0.086
0.092
0.92
0.049
Noon
0.094
0.087
0.089
0.092
0.095
0.095
0.093
0.087
0.095
Evening
0.095
0.087
0.089
0.096
0.096
0.098
0.095
0.087
0.098
Average
0.093
0.087
0.089
0.093
0.093
0.095
0.093
0.087
0.92
PM2.5
Morning
1.044
0.983
1.159
1.036
0.606
0.879
0.637
0.606
1.159
0.88
880 µg/m3
Noon
1.134
1.02
1.777
1.013
0.849
0.779
1.054
0.779
1.777
Evening
0.523
0.625
0.833
0.527
0.509
0.613
0.886
0.509
0.886
Average
0.9
0.876
1.256
0.858
0.654
0.757
0.859
0.65
1.25
PM10
Morning
1.116
1.82
1.167
1.48
0.796
0.899
0.769
0.769
1.82
1.14
1140 µg/m3
Noon
0.21
1.117
1.88
1.182
1.439
1.641
1.135
0.21
1.88
Evening
0.838
0.992
1.326
0.833
0.804
1.201
1.387
0.804
1.387
Average
0.721
1.309
1.457
1.165
1.013
1.249
1.097
0.721
1.45
CH4
Morning
64
59
54
62
54
180
120
54
180
139.36
212.96
Noon
299
66
92
152
174
120
268.06
66
299
Evening
324
103
138
174
192
55.68
176
55.68
224
Average
229
76
94.67
129.33
140
118.56
188.02
76
229
CI
Morning
0
0
0.05
0.05
0.05
0.035
0.02
0
0.05
0.11
0.037
Noon
0
0
0.06
0.06
0.06
0.131
0.02
0
0.131
Evening
0
0
0.06
0.06
0.06
0.155
0.02
0
0.155
Average
0
0
0.056
0.056
0.54
0.131
0.02
0
0.54
NH3
Morning
0.2
0.2
0.1
0
0
0.3
0.2
0
0.3
0.217
0.31
Noon
0.2
0.3
0.3
0
0.3
0.2
0.3
0
0.3
Evening
0.2
0.3
0.3
0.2
0.3
0.31
0.268
0.2
0.3112.6
Average
0.22
0.267
0.233
0.067
0.2
0.277
0.256
0.067
0.277
<xref ref-type="bibr" rid="scirp.144072-"></xref>3.3. Correlation Analysis among the Pollutants (CH<sub>4</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, CO, Cl<sub>2</sub>, NH<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO<sub>2</sub>) of the Study Area
Pearson’s correlation analysis was performed in order to determine the relationships among the gaseous air pollutants (CH4, PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2, CO2) in different areas of Madargonj which has been shown in
Table 3
.
Based on the experimental settings, PM2.5 and SO2 are positively correlated (r = 0.777, p < 0.05). This relationship designates with a concentration increase of PM2.5 in the air, it is expected to see a rise in the value of SO2. PM10 and CH4 (r = −0.891, p < 0.01) are also strongly correlated, but their relationship is in a negative direction. It implies with the increase of PM10 the value of CH4 would likely decrease.
Other than the two mentioned significant relationships between pollutants, A Positive correlation between PM10 and SO2 (r = 0.725, p < 0.05). A positive correlation between PM10 and SO2 means that as the levels of PM2.5 increase, the levels of SO2 also tend to increase, and vice versa. Again, this analysis shows that the negative correlation between SO2 and CO (r = −0.868, p < 0.05). This might suggest different emission sources or atmospheric processes affecting the two pollutants inversely.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Table 3. Pearson Correlation Analysis among the pollutants (CH<sub>4</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, CO, Cl<sub>2</sub>, NH<sub>3</sub>, SO<sub>2</sub>, NO<sub>2</sub>, CO<sub>2</sub>).
pollutants
CO2
NO2
CO
CH4
NH3
SO2
Cl2
PM2.5
PM10
CO2
1
NO2
−0.394
1
CO
−0.687
0.210
1
CH4
0.248
0.581
−0.025
1
NH3
0.451
−0.203
−0.261
−0.065
1
SO2
0.378
−0.379
−0.868*
−0.397
0.268
1
Cl2
−0.147
0.272
0.260
−0.065
−0.116
−0.192
1
PM2.5
0.268
−0.552
−0.650
−0.220
0.060
0.777*
−0.537
1
PM10
−0.046
−0.512
−0.383
−0.891**
0.153
0.725*
−0.154
0.475
1
*. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed).
<xref ref-type="bibr" rid="scirp.144072-"></xref>3.4. Health Impact of People in the Study Area
Exposure assessment
Exposure assessment involves a determination of the size and nature of the population that has been exposed to the toxicant under consideration, and the length of time and toxicant concentration to which they have been exposed.
A) High exposure Population
Total farmers within the study area = 15,725.
The number of people in around one km radius of the study area = 3575.
The number of students within the study area = 5375.
Therefore, the total number of high exposures = 24,675.
B) Moderate Exposure Population
The people outside of the study area = 5750.
The people who live 500 m away around the study area are = 12,875.
Hence, the total number of moderate exposure people = 18,625.
C) Average Exposure
The rest of the people are on average exposure. The number of people = 20,075.
Excess death resulting from exposure to pollutants can be estimated from dose response lap provided by URB ADR urban or quality management strategy in Asus greater Mumbai report-A publication World Bank, shown below.
Excess death = 0.00112 × [pollutants − 41] × P × C
Change in yearly cases of chronic bronchitis per 100,000 people is estimated at 6.12 µg/m3 pollutants. The number of cases = 6.12 × [pollutants − 41] × 15,000
Change respiratory hospital diseases per 100,000 people are estimated at 1.2 using WHO standard, respiratory hospital diseases per 100,000 are estimated at 1.2 × [pollutant − 41] × P/100,000
Change respiratory hospital diseases per 100,000 people are estimated at 1.2 per µg/m3 pollutants using WHO standard, Emergency room visits per 100,000 are estimated at 23.54 [pollutant − 41] × P/100,000
<xref ref-type="bibr" rid="scirp.144072-"></xref>Table 4. Exposure assessment of population in the study area.
Number of populations exposed in the study area
Exposure
Type of population
Survey (No.)
Shamgonj
Milon
Jorkhali
Nandina
Baromaisha
Chor
Krisnopur
Total
type
in the study area
from farmers
kalibari
Bazar
Bhatiyani
Farmers
1
3000
2500
2200
2000
2500
2000
2000
15,725
2
2000
3000
1500
2500
3000
2500
2000
3
2500
3200
2500
1500
2000
2000
1500
4
2000
2000
3000
1500
2500
1500
2500
Avg = 2375
Avg = 2675
Avg = 2350
Avg = 1875
Avg = 2500
Avg = 2000
Avg = 2000
High exposure
People in 1 km
Radius
1
500
500
500
500
300
800
500
3575
2
200
500
500
300
700
500
500
3
300
800
500
200
300
1000
700
4
300
600
800
700
500
500
500
Avg = 325
Avg = 600
Avg = 575
Avg = 425
Avg = 450
Avg = 700
Avg = 550
Students
1
1500
300
800
500
800
500
500
5375
2
1500
500
800
500
800
300
500
3
2000
500
1000
800
500
500
700
4
1500
500
1000
500
700
1000
500
Avg = 1625
Avg = 450
Avg = 900
Avg = 575
Avg = 700
Avg = 575
Avg = 550
Total = 24,675
Moderate
exposure
Outside people
1
2000
1200
1000
500
600
700
1200
5750
2
1500
700
500
700
800
500
1000
3
1000
500
500
300
1000
500
700
4
1000
1000
1000
500
600
800
700
Avg = 1375
Avg = 850
Avg = 750
Avg = 500
Avg = 750
Avg = 625
Avg = 900
People of 500 m away around the study area
1
3000
2500
2000
1500
2500
2000
1000
12,875
2
4000
2000
1500
1000
1800
1500
700
3
3000
1800
2500
1200
2000
1000
800
4
2500
2000
1500
1500
2200
1500
1000
Avg = 3125
Avg = 2075
Avg = 1875
Avg = 1050
Avg = 2125
Avg = 1500
Avg = 1125
Total = 18,625
Average
exposure
Rest of the people
1
5000
4500
4000
1800
3000
2500
1800
20,075
2
4500
4000
2500
2000
2500
2200
2000
3
3000
3000
3500
2500
3200
2500
2500
4
3500
3000
2800
2500
2000
2000
2000
Avg = 4000
Avg = 3625
Avg = 3200
Avg = 2200
Avg = 2675
Avg = 2300
Avg = 2075
As shown in “
Table 4
”, the exposure assessment reveals that a significant portion of the study area’s population is subject to varying degrees of pollution exposure. High exposure groups—comprising farmers, students, and residents within a 1 km radius—total 24,675 individuals. Meanwhile, 18,625 people fall under moderate exposure, including those living 500 meters around the area and those residing outside the immediate vicinity. The remaining 20,075 individuals experience average exposure. This categorization forms the basis for estimating potential health impacts such as excess deaths, bronchitis cases, respiratory hospital admissions, emergency room visits, asthma attacks, and respiratory symptom days.
<xref ref-type="bibr" rid="scirp.144072-"></xref>Table 5. Valuation of health impacts due to pollutants.
Case
Item
High exposure
Moderate
Exposure
Average
exposure
Total
exposure
Mortality
Excess death
68
32
100
200
Morbidity
Bronchitis
480
225
100
805
Restricted activity day
464,519
218,899
94,307
777,725
Respiratory hospital diseases
94
44
19
157
Emergency Room visits
1849
866
384
3099
Asthma attack
18,432
8682
3742
30,856
Respiratory
Symptom days
1,478,382
696,671
300,143
2,475,196
(Source: Field survey, 2023).
Table 5
presents the estimated health impacts caused by pollution across varying exposure levels. High exposure areas recorded the highest number of cases across all health indicators. Excess deaths occurred in the high exposure group, indicating a direct link between pollution levels and mortality. There were 68 excess deaths and 805 bronchitis cases, with over 1,478,382 respiratory symptom days in highly exposed groups. Moderate and average exposure areas also showed significant health burdens, though comparatively lower. Overall, pollution exposure is directly linked to increased mortality, morbidity, hospital visits, and respiratory problems.
4. Discussion
A portable sensor-based air quality measurement system was used in this study to assess the levels of the main air pollutants (PM2.5, PM10, CH4, CO, Cl2, NH3, SO2, NO2, CO2). For this purpose, a total of seven sampling locations were studied on the biomass burning impact in Jamalpur. In this study, it is noteworthy to mention that the concentration of O3, H2S were so negligible in the Jamalpur air that the sensors of the portable monitoring station could not record any values. Also, through spatial distribution, it was remarked the station number containing the highest and the lowest concentration of the pollutants by using various colors whereas red color denotes the lowest concentration and purple color denotes the lowest concentration range of pollutants. Comparing with other studies we found that the concentration of SO2 is comparatively higher than in the study of Nanjing, China (
Hasnain et al., 2023
); Delhi, India (
Tyagi et al., 2016
); Dhaka (
Hoque et al., 2020
). Again CO2 is relatively similar range in the study of Tangail (
Rehnuma et al., 2023
), and higher than Dhaka (
Pavel et al., 2020
) and NO2 is comparatively lower than Delhi, India (
Tyagi et al., 2016
), CH4 is relatively higher than the study of Gajipur city (
Rehnuma et al., 2023
), Chittagong (
Rouf et al., 2012
) and Shanghi, China (
Wei et al., 2020
); PM2.5 is relatively higher than Gajipur (
Mukta et al.,2020
), Narayangonj (
Rahman et al., 2019
). And PM10 is higher than Gajipur (
Mukta et al., 2020
), Chittagong (
Hoque et al., 2022
), Malaysia (
Mohtar et al., 2018
). The elevated PM2.5 and SO2 levels in Madargonj (880 µg/m3 and 7.05 mg/m3, respectively) exceed those in urban Dhaka (125.66 µg/m3) and Gazipur (208 µg/m3), underscoring the acute pollution burden from agricultural burning. The strong PM2.5-SO2 correlation (r = 0.777) suggests shared combustion-driven emission pathways, while the negative PM10-CH4 correlation (r = −0.891) may reflect dispersion dynamics or methane oxidation processes. Additionally, Pearson correlation was conducted to find out a relationship among the pollutants including positive correlation between PM2.5 and SO2 (r = 0.777, p < 0.05) and PM10 and SO2 (r = 0.725, p < 0.05) and negative correlation between SO2 and CO (r = −0.868, p < 0.05) and PM10 and CH4 (r = −0.891, p < 0.01). It was measured that CO₂, CH4 and SO2 are densely concentrated in the sampling location’s air of the observed area causing frequent health hazards of the villagers. From the study, it was estimated various risk factors including bronchitis, restricted activity days, respiratory hospital disease, emergency room visits, asthma attack and respiratory symptom days. At the case of bronchitis, high exposure 480, moderate exposure 225, average exposure 100 and the total exposure 805. Restricted activity days: high exposure 464,519, moderate exposure 218,899, average exposure 94,307 and the total exposure 777,725, Respiratory hospital disease high exposure 94, moderate exposure 44, average exposure 19 and total exposure 157. Emergency room visits, high exposure 1849, moderate exposure 866, average exposure 384 and total exposure 3099. Asthma attack: high exposure 18,432, moderate exposure 8682, average exposure 3742 and total exposure 30,856. For Respiratory symptom days high exposure 1,478,382, moderate exposure 696,671, average exposure 300,143 and total exposure 2,475,196. In case, the people of this area are risked at these air pollutants. This can be minimized by raising awareness about the health risks associated with biomass burning, such as respiratory issues due to air pollution and exposure to hazardous particulate matter and encouraging the use of cleaner burning technologies and the implementation of smoke-free zones. We should encourage the adoption of sustainable biomass burning practices to minimize air pollution. This includes promoting controlled burning techniques and using biomass waste for energy production.
The spatial concentration of monitoring stations within Madargonj Upazila, while sufficient to characterize local pollution patterns, may not fully represent variability in other regions with differing agricultural practices or meteorological conditions. Future studies should incorporate broader geographical coverage and multi-seasonal sampling to enhance generalizability.
Limitations
This study has several limitations: 1) short sampling duration (5 days), 2) reliance on a single sensor model without cross-calibration, and 3) lack of source apportionment to distinguish biomass burning from other emission sources. Addressing these in future work would strengthen causal inferences.
5. Conclusion
This study indicates that similar types of research can be carried out in other regions of the world to mitigate health risks, we recommend: 1) piloting community-based composting of water hyacinth to reduce burning, 2) distributing low-cost particulate masks during burning seasons, and 3) integrating air quality alerts into local healthcare programs. These interventions should be coupled with longitudinal health monitoring to evaluate effectiveness. Future studies should expand to multi-seasonal sampling and incorporate socio-economic confounders to refine policy recommendations. With the help of reliable data being available, more investigation into the pollution caused by burning biomass and its consequences on the environment across our country will be possible. The direction of future research will be decided by the findings of this investigation. It is intended that the information obtained in this study will be helpful in subsequent studies on this subject. In addition, when trustworthy data becomes available, it will support our efforts to carry out additional studies on the pollution caused by burning biomass and its effects on the ecosystem throughout our nation.
Future studies in the realm of meteorology are possible. Future studies can concentrate on determining the primary causes of human interaction with air pollution, as well as the indoor and outdoor air pollution control index of a specific location and a large-scale region, as well as relationships within various atmospheric parameters. Future research on these studies is recommended.
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