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Identification and Apportionment of Sources from Air Particulate Matter at Urban Environments in Bangladesh
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... The burning of fossil fuel and biomass also generates fine particles, PM 2.5 . The emission of particulate matter depends on the design of the vehicle's engine that is whether the engine is under-powered and poorly maintained and if there is fuel loss because of over-fueling [15][16][17]. Additionally, the higher concentration of particulate matter was identified in that areas where motorized vehicle movement was higher like Dhaka City and less in that areas where non-motorized vehicle movement was found or no vehicle [9]. ...
... Additionally, the higher concentration of particulate matter was identified in that areas where motorized vehicle movement was higher like Dhaka City and less in that areas where non-motorized vehicle movement was found or no vehicle [9]. Among others, the brick manufacturing industries are significantly helping in, not only polluting the air but also, deteriorating the surrounding environment due to the usage of traditional kilns which have become outdated and use poor-quality fuels [11, 16,17]. Study discovered a significant correlation between brick kiln surrounding Dhaka and PM 2.5 level [11]. ...
Introduction: The quality of air is becoming progressively worsen day to day. The increasing concentration of air pollution and its associated health effects are rapidly rising in Bangladesh and have drawn attention in recent years. The purpose of the research is to look into the increasing levels of air pollution in Bangladesh, specifically the proportion of Air Quality Index (AQI) in four seasons and six districts, monthly mean AQI, the correlation between PM2.5 and AQI between 2014 and 2019.
Materials and methods: The AQI data from six monitoring stations have been collected for research purposes from Continuous Air Monitoring Stations (CAMS). MS Excel 2020 and IBM SPSS V27 were used for the analysis.
Results: This study reveals that air quality in six districts of Bangladesh has been declining from 2014 to 2019, with winter and monsoon seasons being the most polluted. Dhaka and Narayanganj were found to have the unhealthiest air quality. There is a strong relationship between Paerticulate Matters (PM2.5) and AQI, with AQI increasing with the amount of PM2.5 in all cities. In January, February, March, November, and December, the monthly mean AQI was higher, but in May, June, and July, the mean was lower. The F-values were significant based on seasons and stations. Overall, the study highlights the increasing air pollution and associated health effects in Bangladesh.
Conclusion: Air pollution in Bangladesh is a significant issue due to industrialization, urbanization, transportation, and fuel use, resulting in consistently high AQI levels throughout the year except during rainy months.
... The proportion of the elderly population has increased in the last decades due to increased life expectancy in Bangladesh, contributing to an additional risk factor for air pollution related deaths. About 58% of the air pollution in Dhaka is caused by brick kilns (Begum et al., 2014), with other major contributors including unplanned urbanization, pollutants from 53 large construction projects, and brick kilns (IQAir, 2020a)..Bangladesh has one of the highest levels of exposure to PM2.5, ranking ninth among the top ten countries with the highest level of PM2.5 in outdoor air (SOGA, 2020b). ...
... The CAMS data of the DoE suggest that western divisions of Bangladesh was more polluted than the eastern divisions between 2013 and 2021 although most of the industries are located in eastern region (World Bank 2022). The transboundary air pollution may be the cause of it which was suggested by Begum (Begum et al 2014). Therefore regional coordination is necessary for actual air quality improvements (World Bank 2022). ...
This study aims to review the literature about air pollution, both outdoor and household, and its health consequences in Bangladesh. We searched articles in March 2021 using the PubMed/Scopus database. Peer-reviewed published documents with analytical data and results were retrieved. We also reviewed studies related to ambient and household air quality, the sources of air pollution, the health and economic implications, and the role of NGOs and Govt. of Bangladesh. The sources of ambient air pollution in Bangladesh include Anthropogenic origins like the burning of fossil fuel, coal, wood, open burning of waste or agricultural residues, emission from motor vehicles and industries, use of biomass fuel for cooking, and transboundary air pollution; and Natural sources like windblown dust, sea spray, forest fires, and methane gas emitted by animals. Brick kilns, surface dust, and vehicle emissions contribute about 85.0% of local air pollution in Dhaka, the largest urban area. The contribution from transportation is aggravated by traffic congestion, contaminated fuels, including leaded fuels, two-stroke auto-rickshaws, overloading, and the dust generated due to friction with the roadways. Industries inside cities and brick kilns surrounding the town are also significant contributors. In recent years, the air pollution of Dhaka city has worsened, causing adverse health effects and environmental degradation. The government response has included eliminating the leaded fuel, initiating the use of Compressed Natural Gas (CNG), and phasing out the two-stroke auto-rickshaw, replacing them with CNG driven auto rickshaw. It also implemented and updated regulations, formulated policies and strategic plans.
... Thermal inversions are common during the winter season. Besides, the winter in Bangladesh is very dry because of the dry soil conditions, scanty rainfall, low relative humidity, and low north-westerly winds [11,76]. Previously, both local and transboundary sources were held responsible for air pollution in Dhaka; the highest pollution days were found to be governed by biomass-related PM 2.5 in winter. ...
Trees in urban forests are able to better air quality by removing particulate matter (PM) from the atmosphere through the accumulation of particles on their leaf surfaces. When exposed to air pollutants, the physiology, morphology, and biochemistry of a plant may be affected, which will result in alterations to that plant’s function and growth. In this study, we assessed, for the first time, the tolerance or sensitivity of four evergreen trees (Ficus benghalensis, Ficus religiosa, Mangifera indica, and Polyalthia longifolia) towards air pollution by employing several indices. The trees, which are commonly grown along the roadside in Dhaka, Bangladesh, were evaluated by using the air pollution tolerance index (APTI), the anticipated performance index (API), and the metal accumulation index (MAI). The deposition of four heavy metals (Cd, Cr, Pb, and Ni) on the leaves of four aforementioned tree species was studied employing ICP-MS, and subsequently, a predictive foliar MAI was created. APTI values of the studied plants varied from 10.31 to 12.51 implying that they were either intermediately tolerant or sensitive. A significantly strong positive correlation was obtained between APTI and relative water content (RWC) (r = 0.864; p < 0.001) and between APTI and ascorbic acid content (AAC) (r = 0.748; p < 0.01). The API revealed M. indica as a good performer, which maintained the highest score (68.75%) among the tree species irrespective of different sites. The Pb concentrations were anomalously high in the atmosphere of Dhaka, suggesting its anthropogenic origin. A significant (r = 0.722; p < 0.01) relationship was found between Cd and Pb indicating their common origin. Among the species, F. benghalensis had the highest MAI value (13.60). The MAI value was found to have a significant association with pH, AAC, and total chlorophyll content. Based on APTI, API, and MAI values, the most suitable plant species for urban forest development was identified to be M. indica followed by F. benghalensis and F. religiosa.
... The e ciency of the model was further veri ed by studies to project the future concentrations of ground-level ozone and detected a signi cant increase in the level of ozone during the burning periods under a business-as-usual scenario [20], [21]. Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model was also used to estimate the stubble burning areas that affect the quality of air both in India and other parts of the world [22]- [24]. Additionally, satellite data is used for analyzing aerosol optical depth (AOD), re radiation power (FRP) and other related properties to assess the impact of SB on the air quality in India [14], [28], [29]. ...
The increase in intensity of anthropogenic activities in the world has induced increase in vulnerability to natural and man-made hazards, especially in densely populated metropolitan cities. Delhi faces severe health and infrastructure related issues due to the deteriorated air quality that worsens with variation in seasonal meteorological conditions. This study aims to identify the spatial sources of pollution contributing to Delhi and understand the role of anthropogenic activities and meteorological conditions in pollutant levels of the city in different years and seasons. This study assesses the variation in pollutant levels of CO, NO 2 and SO 2 in Delhi using ground and satellite observation data from 2018 to 2022. The pollutant levels are compared to the meteorological conditions to assess the role of environmental conditions in the change in pollutant levels along with its association to potentially contributing anthropogenic activities in and around Delhi region such as stubble burning in Punjab in 2019. The study further uses meteorological data and pollutant concentration data to develop back trajectories and carry out WCWT analysis that helps us identify the spatial hotspots contributing to the pollutant levels in Delhi on annual and seasonal basis. The exercise is carried out at winter, Pre-Monsoon, Monsoon, Post-Monsoon and Annual timespan for 2019 for 100m, 500m, 1000m and 1500m height above ground level composited to identify the spatially contributing hotspots. This study identi es the clear contributing hotspots in different seasons of 2019 and its overlap with observed active re areas. This can help us segregate areas with similar re intensity and varying contribution extent to pollutant levels of Delhi.
... In Bangladesh, about 35% of the ambient PM 2.5 and 15% of the PM 2.5 are generated from brick kiln emissions and transportation systems [8,56,57]. Emissions from various kinds of poorly maintained vehicles using diesel and petrol are generating PM 2.5 pollutants in the urban areas of Bangladesh [58,59]. ...
The major industrial cities of Bangladesh are experiencing significant air-pollution-related problems due to the increased trend of particulate matter (PM 2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM 2.5 and land use and climatic variables to identify the riskiest areas and population groups using a geographic information system and regression analysis. The results show that about 41% of PM 2.5 concentration (µg/m 3) increased within 19 years (2002-2021) in the study area, while the highest concentration of PM 2.5 was found from 2012 to 2021. The concentrations of PM 2.5 were higher over barren lands, forests, croplands, and urban areas. From 2002-2021, the concentration increased by about 64%, 62.7%, 57%, and 55% (µg/m 3) annually over barren lands, forests, cropland, and urban regions. The highest concentration level of PM 2.5 (84 µg/m 3) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (hPa) (r 2 = −0.26), evaporation (kg m −2) (r 2 = −0.01), humidity (kg m −2) (r 2 = −0.22), rainfall (mm/h) (r 2 = −0.20), and water vapor (kg m −2) (r 2 = −0.03) were negatively correlated with PM 2.5. On the other hand, air temperature (k) (r 2 = 0.24), ground heat (W m −2) (r 2 = 0.60), and wind speed (m s −1) (r 2 = 0.34) were positively correlated with PM 2.5. More than 60 Upazilas were included in the most polluted areas, with a total population of 11,260,162 in the high-risk/hotspot zone (1,948,029 aged 0-5, 485,407 aged 50-69). Governmental departments along with policymakers, stainable development practitioners, academicians, and others may use the main results of the paper for integrated air pollution mitigation and management in Bangladesh as well as in other geographical settings worldwide.
... Air pollution is defined as an atmospheric condition in which substances (air pollutants) are present at concentrations higher than their normal ambient (clean atmosphere) levels to produce measurable adverse effects on humans, animals, vegetation, or materials [54]. Rapid urbanization, vehicle emission, industrialization, brick kilns, biomass burning, transboundary air pollution, etc., are responsible sources of indoor and ambient air pollution [14,22,39,51]. On account of combustion of Sulphur containing fuels, such as coal and crude oils, is a key anthropogenic source of SO2 emissions; CO, PM2.5 directly emitted during incomplete combustion processes with high-temperature conditions from power stations, biomass burning, vehicular and agricultural activity; coarse particles (PM2.5-10) ...
... Air pollution is defined as an atmospheric condition in which substances (air pollutants) are present at concentrations higher than their normal ambient (clean atmosphere) levels to produce measurable adverse effects on humans, animals, vegetation, or materials [54]. Rapid urbanization, vehicle emission, industrialization, brick kilns, biomass burning, transboundary air pollution, etc., are responsible sources of indoor and ambient air pollution [14,22,39,51]. On account of combustion of Sulphur containing fuels, such as coal and crude oils, is a key anthropogenic source of SO2 emissions; CO, PM2.5 directly emitted during incomplete combustion processes with high-temperature conditions from power stations, biomass burning, vehicular and agricultural activity; coarse particles (PM2.5-10) ...
This study aims to analyze the air pollution studies published online from 1995-2020 in Bangladesh. The data of research publications on “air pollution” from the online database were collected with the following search strategy: publications with terms “air pollution,” “air pollutants,” “concentration of particulate matter/aerosol,” or “effects on human health,” “sources of air pollutants” gaseous air pollutants,” and “heavy metals in the air” in their titles for the period of 1995-2020 were collected. The study summarized the characteristics of published documents, the contents and number of citations, and most profiles of authors. This study is based on research on air pollution exposure from relevant sources, such as peer-reviewed articles, proceedings, and national and international reports. In this study, a total of 143 scientific documents were found online. The first publication on air pollution in Bangladesh was revealed in 1995, while the highest number of publications was published in 2019. The years 2018, 2019, and 20 showed a rapid increase in the number of articles published; 15, 19, 16, and 638, respectively. The articles published in the year 2004 received a more significant number of citations (294). In this study, the offline publication has not been counted, and the study covers only the online publication.
... Before explaining the outcomes of this analysis, a basic discussion of the weather pattern in Bangladesh should be provided. In Bangladesh, the year can be divided into four different seasons: winter (December-February), premonsoon (March-May), monsoon (June-September), and postmonsoon (October-November) (B. A. Begum, Nasiruddin, Randall, Sivertsen, & Hopke, 2014) (Begum et al., 2014). The climate of Bangladesh experiences prominent variations in weather during different seasons. ...
We have investigated the effect of meteorological parameters and brick kilns on the seasonal variation of particulate matter (PM) using 4-year (2013-2016) monitoring data of air quality parameters.
... In Bangladesh and its mega-cities, about 35% of ambient PM10 and 15% of PM2.5 are generated from brick kiln emissions and transportation systems [ 8,44,45 ]. Even emissions from diverse kinds of diesel and petrol vehicles 10 of 15 and poorly maintained automobiles are generating air pollution due to PM2.5 pollutants in urban areas of Bangladesh [ 46,47 ]. The concentration of PM2.5 in the atmosphere depends on several anthropogenic factors such as transportation (vehicle movements), industrial (manufacturing plants and mining), cooking and heating activities [ 48 ], and some meteorological factors like wind speed, air relative humidity, cloud cover, and ambient temperature [ 49 ]. ...
The major industrial cities of Bangladesh are heavily experiencing air pollution-related problems due to the increased trend of Particulate Matter (PM2.5) and other pollutants. This paper aimed to investigate and understand the relationship between PM2.5 and land use and climatic variables and to identify the riskiest area and population groups using a Geographic information system and regression analysis. The results show that about 41% of PM2.5 concentration increased within 19 years (2002-2021) in the study area, while the highest concentration of PM2.5 was found from 2012 to 2021. The concentrations of PM2.5 were higher over barren lands, forests, croplands, and urban areas. About 64%, 62.7%, 57%, and 55% concentrations were increased annually over barren lands, forests, cropland, and urban regions, respectively, from 2002-2021. The highest concentration level of PM2.5 (84 mg m-3) among other land use classes was found in urban areas in 2021. The regression analysis shows that air pressure (r2= - 0.26), evaporation (r2= - 0.01), humidity (r2= - 0.22), rainfall (r2= - 0.20), and water vapor (r2= - 0.03) were negatively correlated with PM2.5. On the other hand, air temperature (r2= 0.24), ground heat (r2= 0.60), and wind speed (r2= 0.34) were positively correlated with PM2.5. More than 60 Upazilas were the most polluted areas, with 1,948,029 populations (ages 0-5), 485,407 (ages 50-69), and a total population of 11,260,162 were in the high-risk/hotspot zone. The government line department may use the main results paper's key results, policymakers, stainable development practitioners, academicians, and others for integrated air pollution mitigation and management in Bangladesh and other geographical settings worldwide.
... notably, although national manufacturing and industrial activities are more concentrated in the eastern divisions of the country while the western is more agriculturally intensive, air pollution levels are higher in the west than the east. This may be explained, at least to some extent, by transboundary air pollution as suggested by findings from Begum et al. (2014). Estimates indicate that 40 percent of total PM 2.5 concentration in Khulna originates from outside of the country, for example (World Bank, forthcoming b). ...
Exposure to air pollution is the fourth leading risk factor for premature deaths, globally. Most of these deaths are caused by the inhalation of fine particulate matter (PM2.5). Bangladesh is particularly vulnerable to the effects of air pollution, with its capital Dhaka ranked high among the most polluted cities in the world. While there is global literature that link the relationship between air pollution and health, evidence from Bangladesh is scanty. Hence, there is a need to better understand the air pollution levels in the country and document its interaction with human health for better preparedness.
Combining primary data from Bangladesh with global evidence, this seminal book establishes the relationship between air pollution and physical and mental health. Individual level data was collected from two cities in Bangladesh – Dhaka, the most polluted part of the country, and Sylhet, the least polluted. Dhaka is further stratified into three sites based on PM2.5 concentration levels: persistent traffic, major construction and traffic, brick kilns and comparator (rural Sylhet).
Highest concentrations of PM2.5 are found in sites with major construction and traffic, at 150 percent above the threshold for Air Quality Guidelines (AQG) of the World Health Organization (WHO), followed by brick kilns (136 percent above the WHO AQG). There is a strong association between exposure to PM2.5 and health risks such as breathing difficulties, cough, respiratory infections and depression. The most vulnerable are children, the elderly, and people with underlying health conditions. These same cohorts living in areas with major construction and traffic are more susceptible to the health risks due to outdoor air pollution than other areas.
There is a synergistic relationship between climate change and air pollution. With climate change projected to aggravate and thus worsen air quality further, it is critical for countries like Bangladesh to implement adaption measures.
In Dhaka, Bangladesh, particular matter (PM) is the air pollutant that is most harmful to public health and the environment when compared to other measured criteria pollutants. During recent years, the Government of Bangladesh has tried to control PM emissions coming from anthropogenic sources. About 30-50% of the PM10 mass in Dhaka (depending on location) is in fine particles with aerodynamic diameter less than 2.2 mu m. These particles are mainly of anthropogenic origin and predominately from transport-related sources. However, the combination of meteorological conditions, long-range transport during the winter and local sources results in PM concentrations remaining much higher than the Bangladesh National Ambient Air Quality Standard (BNAAQS). It has been found that black carbon accounted for about 50% of the total fine PM mass before the adoption of control policies. As a result, the PM emission as well as BC has not increased in proportion to the increase in the number of combustion sources like motor vehicles, diesel power generator or brick kiln. Positive Matrix Factorization (PMF) was applied to fine particle composition data from January 2007 to February 2009. It was found that motor vehicles contribute less BC with respect to brick kiln industry. This result demonstrates the effectiveness of the government's policy interventions since previously vehicles represented the major contributors of BC. BC is also transported over long distances, mixing with other particles along the way as demonstrated by a potential source contribution function analysis. Transboundary transport of air pollution in the South Asian region has become an issue of increasing importance over the past several decades. The relative amounts of local and long-range transported pollutants are currently unknown.
The fine particle (<2.5 μm) composition data from seven National
Park Service locations in Alaska for the period from 1986 to 1995 was
performed using a new type of factor analysis, positive matrix
factorization (PMF). This method uses the estimates of the error in the
data to provide optimum data point scaling and permits a better
treatment of missing and below detection limit values. Eight source
components were obtained for data sets from the Northwest Alaska Areas
and the Bering Land Bridge sites. Five to seven components were obtained
for the other Alaskan sites. The solutions were normalized by using
aerosol fine mass concentration data. Squared correlation coefficients
between the reconstructed mass obtained from aerosol composition data
for the sites and the measured mass were in the range of 0.74-0.95. Two
factors identified as soils were obtained for all of the sites.
Concentrations for these factors for most of the sites have maxima in
the summer and minima in the winter. A sea-salt component was found at
five locations. A factor with the highest concentrations of black carbon
(BC), H+, and K identified as forest fire smoke was obtained
for all data sets except at Katmai. Factors with high concentrations of
S, BC-Na-S, and Zn-Cu were obtained at all sites. At three sites, the
solutions also contained a factor with high Pb and Br values. The
factors with the high S, Pb, and BC-Na-S values at most sites show an
annual cycle with maxima during the winter-spring season and minima in
the summer. The seasonal variations and elemental compositions of these
factors suggest anthropogenic origins with the spatial pattern
suggesting that the sources are distant from the receptor sites. The
seasonal maxima/minima ratios of these factors were higher for more
northerly locations. Four main sources contribute to the observed
concentrations at these locations: long-range transported anthropogenic
aerosol (Arctic haze aerosol), sea-salt aerosol, local soil dust, and
aerosol with high BC concentrations from regional forest fires or local
wood smoke. A northwest to southeast negative gradient suggesting
long-range transport of air masses from regions north or northwest of
Alaska dominated the spatial distribution of the high S factor
concentrations.
Fine particulate matter (PM2.5) samples were simultaneously collected on Teflon and quartz filters between February 2010 and February 2011 at an urban monitoring site (CAMS2) in Dhaka, Bangladesh. The samples were collected using AirMetrics MiniVol samplers. The samples on Teflon filters were analyzed for their elemental composition by PIXE and PESA. Particulate carbon on quartz filters was analyzed using the IMPROVE thermal optical reflectance (TOR) method that divides carbon into four organic carbons (OC), pyrolized organic carbon (OP), and three elemental carbon (EC) fractions. The data were analyzed by positive matrix factorization using the PMF2 program. Initially, only total OC and total EC were included in the analysis and five sources, including road dust, sea salt and Zn, soil dust, motor vehicles, and brick kilns, were obtained. In the second analysis, the eight carbon fractions (OC1, OC2, OC3, OC4, OP, EC1, EC2, EC3) were included in order to ascertain whether additional source information could be extracted from the data. In this case, it is possible to identify more sources than with only total OC and EC. The motor vehicle source was separated into gasoline and diesel emissions and a fugitive Pb source was identified. Brick kilns contribute 7.9 microg/m3 and 6.0 microg/m3 of OC and EC, respectively, to the fine particulate matter based on the two results. From the estimated mass extinction coefficients and the apportioned source contributions, soil dust, brick kiln, diesel, gasoline, and the Pb sources were found to contribute most strongly to visibility degradation, particularly in the winter.
Samples of fine and coarse airborne particulate matter (PM) were collected between February and July 2007 at the Continuous Air Monitoring Station (CAMS) in Chittagong, the second largest city in Bangladesh. Samples were collected using a dichotomous sampler in two fractions of < 2.5 mu m (fine) and 2.5 to 10 mu m (coarse). Samples were analyzed for elemental concentrations by proton induced X-ray emission (PIXE), hydrogen by proton elastic scattering analysis (PESA), and black carbon by reflectometry. Elemental data sets together with black carbon were analyzed by positive matrix factorization to identify the possible sources of mass for the coarse and fine PM fractions. Best solutions were found to be six and seven factors for elemental compositions for coarse and fine fractions at the CAMS at Chittagong, respectively. Sources were identified as biomass burning/brick kiln, soil dust, road dust, Zn source (including two-stroke motorcycles), motor vehicle, CNG vehicle, and sea salt. The PMF results show that about 35.5% of PM(2.5) mass at this site comes from biomass burning. The second largest contribution of fine PM comes from motor vehicle including CNG vehicles. The third one is a Zn source that includes emissions from two-stroke vehicles and galvanizing factories with vehicles, probably the larger contributor of the two source types. In case of coarse PM, about 40% of PM(2.5-10) mass comes from soil dust including road dust.
Transboundary transport of air pollution in the South Asian region has been an issue of increasing importance over the past several decades. Long-range transport of anthropogenic pollution is contrasted with that of pollution produced by natural processes such as dust storms or natural forest fires. Airborne particulate matter datasets covering the period from 2002 to 2007 from the neighboring countries like Bangladesh, India, Pakistan and Sri Lanka were used to find the source areas that are primarily responsible for long range transported pollutants. All four countries collected samples with the same type of sampler and follow the same technique for mass and BC measurements. It was found that high fine soil contributions were from dust storms. On the other hand, smoke in this region mainly comes from northern India where agricultural waste is often burned. (C) Author(s) 2011. This work is distributed under the Creative Commons Attribution 3.0 License.
Particulate matter (PM) sampling for both coarse and fine fractions was conducted in a semi-residential site (AECD) in Dhaka from February 2005 to December 2006. The samples were analyzed for mass, black carbon (BC), and elemental compositions. The resulting data set were analyzed for sources by Positive Matrix Factorization (EPA-PMF). From previous studies, it is found that, the air quality became worse in the dry winter period compared to the rainy season because of higher particulate matter concentration in the ambient air.. Therefore, seasonal source contributions were determined from seasonally segregated data using EPA-PMF modeling so that further policy interventions can be undertaken to improve air quality. From the source apportionment results, it is observed that vehicular emissions and emission from brick kiln are the major contributors to air pollution in Dhaka especially in the dry seasons, while contribution from emissions from metal smelters increases during rainy seasons. The Government of Bangladesh is considering different interventions to reduce the emissions from those sources by adopting conversion of diesel/petrol vehicles to CNG, increasing traffic speed in the city and by introducing green technologies for brick production. However, in order to reduce the transboundary effect it is necessary to take action regionally. (C) 2010 Elsevier Ltd. All rights reserved.
A total of 1,000 kilns producing 3.5 billion bricks and consuming 0.85 million tons of coal per year resulted in an estimated 2,200 to 4,000 premature deaths and 0.2 to 0.5 million asthma attacks per year in the Greater Dhaka region. In this paper, the emission reductions and health cost savings are presented for moving to cleaner brick manufacturing technologies for the districts of Gazipur, Savar, Dhamrai, Rupganj, Manikganj, Kaliganj, and Narayanganj. A summary of various technologies and feasibility of these technologies based on lessons learnt from the pilots is discussed. We explored three “what-if” scenarios through 2020 for better energy efficiency, lower coal consumption, and lower emission rates, under which the total health cost savings are estimated to range between USD12 million annually for short-term implementation and up to 55 million annually for long-term implementation. Between 2015 and 2020, the cumulative health cost savings could range between USD126 and 234 million, which clearly outweigh any cost of capital investment necessary for the technology change. An improvement in energy efficiency will result in USD1.8 to 3.0 million per year in coal savings, which will accrue to the kiln owners collectively, and these savings will pay back the capital investment within 3–4 years, in addition to the health cost savings for the city inhabitants. Hence, the entrepreneurs have all the social, environmental, and economic incentives to adopt cleaner technologies. A major gap at the regulatory level is in building awareness for the entrepreneurs and setting up an incentive structure to implement this transition, which is being addressed by an advisory committee by the Government of Bangladesh responsible for the revision of the Brick Burning Act of 1989 and related legislations.
A new variant ‘PMF’ of factor analysis is described. It is assumed that X is a matrix of observed data and σ is the known matrix of standard deviations of elements of X. Both X and σ are of dimensions n × m. The method solves the bilinear matrix problem X = GF + E where G is the unknown left hand factor matrix (scores) of dimensions n × p, F is the unknown right hand factor matrix (loadings) of dimensions p × m, and E is the matrix of residuals. The problem is solved in the weighted least squares sense: G and F are determined so that the Frobenius norm of E divided (element-by-element) by σ is minimized. Furthermore, the solution is constrained so that all the elements of G and F are required to be non-negative. It is shown that the solutions by PMF are usually different from any solutions produced by the customary factor analysis (FA, i.e. principal component analysis (PCA) followed by rotations). Usually PMF produces a better fit to the data than FA. Also, the result of PF is guaranteed to be non-negative, while the result of FA often cannot be rotated so that all negative entries would be eliminated. Different possible application areas of the new method are briefly discussed. In environmental data, the error estimates of data can be widely varying and non-negativity is often an essential feature of the underlying models. Thus it is concluded that PMF is better suited than FA or PCA in many environmental applications. Examples of successful applications of PMF are shown in companion papers.