This statistic shows the biggest cities in Bangladesh in 2022. In 2022, approximately ***** million people lived in Dhaka, making it the biggest city in Bangladesh.

Bangladesh BD: Population in Largest City data was reported at 23,935,652.000 Person in 2024. This records an increase from the previous number of 23,209,616.000 Person for 2023. Bangladesh BD: Population in Largest City data is updated yearly, averaging 7,344,419.000 Person from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 23,935,652.000 Person in 2024 and a record low of 507,921.000 Person in 1960. Bangladesh BD: Population in Largest City data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the urban population living in the country's largest metropolitan area.;United Nations, World Urbanization Prospects.;;

Actual value and historical data chart for Bangladesh Population In Largest City

Bangladesh BD: Population in Largest City: as % of Urban Population data was reported at 33.448 % in 2024. This records an increase from the previous number of 33.444 % for 2023. Bangladesh BD: Population in Largest City: as % of Urban Population data is updated yearly, averaging 30.733 % from Dec 1960 (Median) to 2024, with 65 observations. The data reached an all-time high of 33.448 % in 2024 and a record low of 19.085 % in 1960. Bangladesh BD: Population in Largest City: as % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Population and Urbanization Statistics. Population in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.;United Nations, World Urbanization Prospects.;Weighted average;

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Other Financial Intermediaries for Bangladesh (BGDFCBMLNUM) from 2004 to 2015 about intermediaries, branches, Bangladesh, and financial.

The population density in Bangladesh reached its highest in 2020, amounting to approximately 1.27 thousand people per square kilometer. The South Asian country was the tenth most densely populated country in the world in 2019. Within the Asia Pacific region, Bangladesh’s population density was only exceeded by Macao, Singapore, Hong Kong, and the Maldives. Overall, Asia had the highest population density in the world in 2018.

Population growth in Bangladesh

In 1971, Bangladesh gained its independence from Pakistan. Bangladesh’s birth rate and mortality rate had declined significantly in the past years with a life expectancy of 72.59 years in 2019. In general, the population in Bangladesh had been growing at a slow pace, slightly fluctuating around an annual rate of one percent. This growth was forecasted to continue, although it was estimated to halve by 2040. As of today, Dhaka is the largest city in Bangladesh.

Population density explained

According to the source, “population density is the mid-year population divided by land area in square kilometers.” Further, “population is based on the de facto definition of population, which counts all residents.” Bangladesh’s population reached an estimated number of 164.69 million inhabitants in 2020. In 2018, the country’s land area amounted 130.2 thousand square kilometers.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

Graph and download economic data for Geographical Outreach: Number of Branches in 3 Largest Cities, Excluding Headquarters, for Deposit Taking Microfinance Institutions (MFIs) for Bangladesh (BGDFCBODMFLNUM) from 2004 to 2015 about microfinance, branches, Bangladesh, and deposits.

The main objective of the 2019 Chattogram for Low Income Area Gender, Inclusion, and Poverty (CITY) study is to collect primary data from male and female residents in slum and non-slum poor neighborhoods in Chattogram, the second largest city of Bangladesh, and build the evidence base about their constraints to access more and better jobs. The CITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh as well as to identify key constraints and solutions for low-income women trying to obtain better jobs.

A broad array of information was collected on issues related to women's economic empowerment, ranging from demographic and socioeconomic characteristics to detailed work history, time use, attitudes about work, and perceptions of work. The key feature of this survey is to collect economic data directly from the main household members, generally the main couples, unlike traditional surveys which only interviewed the heads of households (who tend to be men in most cases); thus, failed to gather valuable information from the female population.

📦 GeoPackage Dataset Overview: This dataset contains information about city corporations, municipalities, divisions, and districts in Bangladesh. It provides detailed data on various aspects of each city corporation, including geographical coordinates, administrative divisions, council seats, population demographics, and other relevant information. The dataset is updated as of May 21, 2023.

🔗 Dataset Collection: Bangladesh Bureau of Statistics

🏙️ "City Corporation" GeoPackage dataset: - 🌍 X (Numeric): X-coordinate or longitude of the city corporation location. - 🌍 Y (Numeric): Y-coordinate or latitude of the city corporation location. - 🗺️ geom (Geometry): Geospatial geometry representing the city corporation's boundary. - 🏢 GEO_CODE (Text): Unique geographical code for the city corporation. - 🏞️ DIVISION_CODE (Text): Code representing the division to which the city corporation belongs. - 🏞️ DIVISION_NAME (Text): Name of the division to which the city corporation belongs. - 🌆 DISTRICT_CODE (Text): Code representing the district where the city corporation is located. - 🌆 DISTRICT_NAME (Text): Name of the district where the city corporation is located. - 🏢 CITY_CODE (Text): Code for the city corporation. - 🏢 CITY_NAME (Text): Name of the city corporation. - 🪑 TOTAL_DIVCC (Numeric): Total number of Divisional City Corporation Council seats. - 🪑 TOTAL_DCC (Numeric): Total number of District City Corporation Council seats. - 🪑 TOTAL_UCC (Numeric): Total number of Upazila City Corporation Council seats. - 🪑 TOTAL_ZCC (Numeric): Total number of Zila City Corporation Council seats. - 🪑 TOTAL_SV (Numeric): Total number of city corporation City Council seats (Special). - 🪑 TOTAL_EM (Numeric): Total number of elected mayors in the city corporation. - 🪑 TOTAL_EA (Numeric): Total number of elected councilors in the city corporation. - 🏠 QUICK_COUNT_HH (Numeric): Quick count of households in the city corporation. - 🏠 ENUMERATED_HH (Numeric): Enumerated count of households in the city corporation. - 📊 COMPLETION (Text): Status of data completion for the city corporation dataset. - 🕒 LAST_UPDATE_TIME (Timestamp): Date and time of the last dataset update. - 👫 TOTAL_POP (Numeric): Total population of the city corporation. - 👨 TOTAL_MALE (Numeric): Total male population in the city corporation. - 👩 TOTAL_FEMALE (Numeric): Total female population in the city corporation. - ⚖️ SEX_RATIO (Numeric): Sex ratio in the city corporation (males per 100 females). - 🏠 HH_SIZE (Numeric): Average household size in the city corporation. - 🌐 TOTAL_NRB (Numeric): Total number of non-resident Bangladeshis (NRBs) in the city corporation.

🏢 "District" GeoPackage dataset: - 🌍 X (Numeric): X-coordinate or longitude of the district location. - 🌍 Y (Numeric): Y-coordinate or latitude of the district location. - 🗺️ geom (Geometry): Geospatial geometry representing the district's boundary. - 🏢 GEO_CODE (Text): Unique geographical code for the district. - 🏞️ DIVISION_CODE (Text): Code representing the division to which the district belongs. - 🏞️ DIVISION_NAME (Text): Name of the division to which the district belongs. - 🌆 DISTRICT_CODE (Text): Code representing the district. - 🌆 DISTRICT_NAME (Text): Name of the district. - 🪑 TOTAL_DIVCC (Numeric): Total number of Divisional City Corporation Council seats. - 🪑 TOTAL_DCC (Numeric): Total number of District City Corporation Council seats. - 🪑 TOTAL_UCC (Numeric): Total number of Upazila City Corporation Council seats. - 🪑 TOTAL_ZCC (Numeric): Total number of Zila City Corporation Council seats. - 🪑 TOTAL_SV (Numeric): Total number of city corporation City Council seats (Special). - 🪑 TOTAL_EM (Numeric): Total number of elected mayors in the district. - 🪑 TOTAL_EA (Numeric): Total number of elected councilors in the district. - 🏠 QUICK_COUNT_HH (Numeric): Quick count of households in the district. - 🏠 ENUMERATED_HH (Numeric): Enumerated count of households in the district. - 📊 COMPLETION (Text): Status of data completion for the district dataset. - 🕒 LAST_UPDATE_TIME (Timestamp): Date and time of the last dataset update. - 👫 TOTAL_POP (Numeric): Total population of the district. - 👨 TOTAL_MALE (Numeric): Total male population in the district. - 👩 TOTAL_FEMALE (Numeric): Total female population in the district. - ⚖️ SEX_RATIO (Numeric): Sex ratio in the district (males per 100 females). - 🏠 HH_SIZE (Numeric): Average household size in the district. - 🌐 TOTAL_NRB (Numeric): Total number of non-resident Bangladeshis (NRBs) in the district.

🏙️ "Municipality" GeoPackage dataset: - 🌍 X (Numeric): X-coordinate or longitude of the municipality location. - 🌍 Y (Numeric): Y-coordinate or latitude of the municipality location. - 🗺️ geom (Geometry): Geospatial geometry representing the municipality's ...

最大城市人口占城市总人口的百分比在12-01-2024达33.448%，相较于12-01-2023的33.444%有所增长。最大城市人口占城市总人口的百分比数据按年更新，12-01-1960至12-01-2024期间平均值为30.733%，共65份观测结果。该数据的历史最高值出现于12-01-2024，达33.448%，而历史最低值则出现于12-01-1960，为19.085%。CEIC提供的最大城市人口占城市总人口的百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的孟加拉 – Table BD.World Bank.WDI: Population and Urbanization Statistics。

Abstract

The 2018 Dhaka Low Income Area Gender, Inclusion, and Poverty (DIGNITY) survey attempts to fill in the data and knowledge gaps on women's economic empowerment in urban areas, specifically the factors that constrain women in slums and low-income neighborhoods from engaging in the labor market and supplying their labor to wage earning or self-employment. While an array of national-level datasets has collected a wide spectrum of information, they rarely comprise all of the information needed to study the drivers of Female Labor Force Participation (FLFP). This data gap is being filled by the primary data collection of the specialized DIGNITY survey; it is representative of poor urban areas and is specifically designed to address these limitations. The DIGNITY survey collected information from 1,300 urban households living in poor areas of Dhaka in 2018 on a range of issues that affect FLFP as identified through the literature. These range from household composition and demographic characteristics to socioeconomic characteristics such as detailed employment history and income (including locational data and travel details); and from technical and educational attributes to issues of time use, migration history, and attitudes and perceptions.

The DIGNITY survey was designed to shed light on poverty, economic empowerment, and livelihood in urban areas of Bangladesh. It has two main modules: the traditional household module (in which the head of household is interviewed on basic information about the household); and the individual module, in which two respondents from each household are interviewed individually. In the second module, two persons - one male and one female from each household, usually the main couple, are selected for the interview. The survey team deployed one male and one female interviewer for each household, so that the gender of the interviewers matched that of the respondents. Collecting economic data directly from a female and male household member, rather than just the head of the household (who tend to be men in most cases), was a key feature of the DIGNITY survey.

Geographic coverage

The DIGNITY survey is representative of low-income areas and slums of the Dhaka City Corporations (North and South, from here on referred to as Dhaka CCs), and an additional low-income site from the Greater Dhaka Statistical Metropolitan Area (SMA).

Analysis unit

• Household
• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling procedure followed a two-stage stratification design. The major features include the following steps (they are discussed in more detail in a copy of the study's report and the sampling document located in "External Resources"):

FIRST STAGE: Selection of the PSUs

Low-income primary sampling units (PSUs) were defined as nonslum census enumeration areas (EAs), in which the small-sample area estimate of the poverty rate is higher than 8 percent (using the 2011 Bangladesh Poverty Map). The sampling frame for these low-income areas in the Dhaka City Corporations (CCs) and Greater Dhaka is based on the population census of 2011. For the Dhaka CCs, all low-income census EAs formed the sampling frame. In the Greater Dhaka area, the frame was formed by all low-income census EAs in specific thanas (i.e. administrative unit in Bangladesh) where World Bank project were located.

Three strata were used for sampling the low-income EAs. These strata were defined based on the poverty head-count ratios. The first stratum encompasses EAs with a poverty headcount ratio between 8 and 10 percent; the second stratum between 11 and 14 percent; and the third stratum, those exceeding 15 percent.

Slums were defined as informal settlements that were listed in the Bangladesh Bureau of Statistics' slum census from 2013/14. This census was used as sampling frame of the slum areas. Only slums in the Dhaka City Corporations are included. Again, three strata were used to sample the slums. This time the strata were based on the size of the slums. The first stratum comprises slums of 50 to 75 households; the second 76 to 99 households; and the third, 100 or more households. Small slums with fewer than 50 households were not included in the sampling frame. Very small slums were included in the low-income neighborhood selection if they are in a low-income area.

Altogether, the DIGNITY survey collected data from 67 PSUs.

SECOND STAGE: Selection of the Households

In each sampled PSU a complete listing of households was done to form the frame for the second stage of sampling: the selection of households. When the number of households in a PSU was very large, smaller sections of the neighborhood were identified, and one section was randomly selected to be listed. The listing data collected information on the demographics of the household to determine whether a household fell into one of the three categories that were used to stratify the household sample:

i) households with both working-age male and female members; ii) households with only a working-age female; iii) households with only a working-age male.

Households were selected from each stratum with the predetermined ratio of 16:3:1. In some cases there were not enough households in categories (ii) and (iii) to stick to this ratio; in this case all of the households in the category were sampled, and additional households were selected from the first category to bring the total number of households sampled in each PSU to 20.

The total sample consisted of 1,300 households (2,378 individuals).

Sampling deviation

The sampling for 1300 households was planned after the listing exercise. During the field work, about 115 households (8.8 percent) could not be interviewed due to household refusal or absence. These households were replaced with reserved households in the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaires for the survey were developed by the World Bank, with assistance from the survey firm, DATA. Comments were incorporated following the pilot tests and practice session/pretest.

Cleaning operations

Collected data was entered into a computer by using the customized MS Access data input software developed by Data Analysis and Technical Assistance (DATA). Once data entry was completed, two different techniques were employed to check consistency and validity of data as follows:

1. Five (5%) percent of the filled-in questionnaire was checked against entered data to measure the transmission error or typos, and;
2. A logical consistency checking technique was employed to identify inconsistencies using SPSS and or STATA software.

The SHAHAR (Supporting Household Activities for Health, Assets and Revenue) project aims to increase incomes and improve unhygienic environments and sanitation conditions in poor urban communities, including slums, in major selected secondary cities in Bangladesh. Using relevant indicators namely socioeconomic; health and hygiene; nutrition and diet; and governance and social institutions the project provides insight into the livelihood conditions and the livelihood security of these slum households. The SHAHAR baseline survey was conducted in July to August, 2002 in slum (basti) communities in Dinajpur. The SHAHAR census survey suggested that only 52 percent of households had children under 5 years of age; doubling the sample size would ensure that the final sample would have at least this many households with children under 5 years of age. This gave an estimated sample size of 271 * 2 = 542. As slum dwellers are assumed to be highly mobile, and the losses would increase over the year between round I in July – August, 2002 and round III a year later, a substantial reserve was added to the sample, as well as some allowance for refusal or other non-response, for a final total of 614 households. The field survey was carried out by two teams, each consisting of three male and female pairs and a supervisor. One pair interviewed one household at a time, with the female interviewer interviewing the main female member of the household and collecting information on household composition, migration and education; training; savings; loans; food consumption; household food security; health; anthropometry and childcare; environment, water and sanitation; shocks and coping strategies; social capital; crime, violence, physical security; governance; and women’s status. The male interviewer interviewed the main male member of the household asking questions on language, religion, and migration; training; employment; transfers, social assistance and other income; household assets; land own ership and tenure; urban agriculture; savings; loans; housing; non food expenditure; shocks and coping strategies; social capital; crime, violence and physical security; and governance. The pairs on each team worked together to cover three households per day and the 614 households surveyed in approximately 39 days in July to August, 2002 in Dinajpur. The SHAHAR (Supporting Household Activities for Health, Assets and Revenue) Dinajpur baseline survey was conducted in collaboration with Data Analysis and Technical Assistance (DATA), Dhaka, Bangladesh; and CARE-Bangladesh. The questionnaires were developed by the International Food Policy Research Institute. The funding for the survey was provided by CARE-Bangladesh; and US Agency for International Development (USAID).

Bangladesh Market Outlook

The Bangladesh market size is projected to grow significantly, with a Compound Annual Growth Rate (CAGR) of 6.5% from 2023 to 2032. The global market size for 2023 is $411 billion, and it is forecasted to reach approximately $685 billion by 2032. This growth is driven primarily by an expanding industrial base, increased foreign investment, and a burgeoning middle class with rising disposable incomes.

One of the key growth factors for the Bangladesh market is the robust expansion of the textiles and garments industry. As one of the largest sectors in the country, it accounts for a significant portion of export revenue and employment. Favorable government policies, such as tax incentives and the establishment of Special Economic Zones (SEZs), have spurred further investment and production capacity in this sector. Additionally, the global shift towards sustainable and ethical fashion has created opportunities for Bangladeshi manufacturers to capture new market shares by adhering to international standards and certification.

Another critical factor contributing to the market growth is the rapid development of the pharmaceutical industry. Bangladesh has emerged as a significant player in the global pharmaceutical landscape, primarily due to its ability to produce generic drugs at competitive prices. The industry has benefited from consistent government support, including relaxed regulatory frameworks and incentives for research and development. Furthermore, the COVID-19 pandemic has highlighted the need for robust healthcare infrastructure, leading to increased investment in medical facilities and pharmaceutical production capabilities.

The burgeoning Information Technology (IT) sector is also a major growth driver. With a young, tech-savvy population, Bangladesh has seen a surge in IT-related activities, including software development, IT services, and business process outsourcing (BPO). Government initiatives such as the Digital Bangladesh Vision 2021 have played a pivotal role in fostering an environment conducive to IT growth. This push towards digital transformation has also attracted significant foreign direct investment (FDI), further bolstering the sector.

Regionally, Dhaka remains the economic powerhouse of Bangladesh, contributing a substantial portion to the country's GDP. The city has seen significant infrastructure development, including the construction of metro lines and expressways, which facilitate business operations and attract investments. Chittagong, as a major port city, also plays a crucial role in the country's trade dynamics, handling a large volume of imports and exports. Khulna and Rajshahi, while smaller in comparison, are growing economic centers with increasing industrial activities and investment opportunities.

Industry Analysis

Textiles and Garments Analysis

The textiles and garments industry is the cornerstone of Bangladesh's economy, accounting for about 84% of the country’s total exports. This sector has experienced exponential growth due to several favorable conditions, including abundant labor supply, competitive labor costs, and supportive government policies. The establishment of Special Economic Zones (SEZs) has provided a controlled environment for businesses to operate efficiently, thereby attracting foreign investors. Additionally, the global trend towards sustainable and ethical production practices has led Bangladeshi manufacturers to adopt international standards, thereby opening new export markets.

Bangladesh's comparative advantage in this sector lies in its ability to produce garments at a lower cost compared to other countries. The availability of a large, skilled, and semi-skilled workforce has made it possible for manufacturers to produce high volumes at competitive prices. Moreover, government incentives such as tax holidays, subsidies, and reduced import duties on raw materials have further fueled growth in this sector. The implementation of advanced technologies like automated sewing machines and cutting-edge design software has also enhanced production efficiency and quality.

The industry's growth is not without its challenges. Issues such as poor labor conditions, safety concerns, and environmental impacts have drawn international scrutiny. However, significant strides have been made to address these issues through initiatives like the Bangladesh Accord and the Alliance for Bangladesh Worker Safety, which aim to improve factory safety and workers' rights. Additionally, th

Abstract

The Bangladesh Urban Informal Settlements Survey (UIS) 2016’s main objective is to collect detailed consumption data from urban slums households following the same methodology used by the Bangladesh Bureau of Statistics (BBS) to collect household consumption data to construct official poverty estimates using the Household Income and Expenditure Survey (HIES).

Analysis unit

Slum household

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey collected data from a total of 600 urban slum households in the Dhaka City Corporation - 10 slum households from 57 medium and large size slum communities, and 5 slum households from a total of 6 small size slum communities. The sampling frame for this study came from the 2014 BBS Census of Slums and Floating Population. Urban slums or slum communities were classified into three different strata - small size slums (5-10 households); medium size slums (11-200 households); and large size slums (more than 200 households). These three strata were not all used as domains of study but rather defined based on field logistics operations - each stratum followed a slightly different field operation strategy to account for the fact that finding, listing, and interviewing households in small slums posed very different challenges for the field implementation than interviewing households in medium or large slums.

The Primary Sampling Units (PSU) in the survey were the slum communities. There were a total of 3,360 slum communities with more than 5 households in the Dhaka City Corporation. PSUs were equally allocated between strata 1 and 2 (small and medium size slum communities) and stratum 3 (large slum communities) using a combination of PPS and practical allocation. More specifically, PSUs were allocated across strata using PPS with the number of slum households used as the measure of size and rounding to account for the pre-determined cluster size of 10 slum households per PSU for medium size slum communities and 5 slum households per PSU for small slum communities. Using this rule we got an allocation of PSUs which very closely resembled the distribution of slum households across the three strata. Table 1 in the sampling document provided under Related Materials tab reports the number of slum communities and households in the BBS 2014 Census of Slums and Floating Population and the UIS sample by stratum. In the second sampling stage, the list of households in selected slum communities was updated as part of the field work. Using this updated list of households, 5 or 10 households were selected from each slum community using systematic equal probability sampling.

As mentioned previously, the selection of slum communities and households followed slightly different field implementation strategies across strata in three dimensions - creation of replacement slum communities, listing exercise, and selection of households. In terms of the creation of replacement slum communities, the first stage sampling included a ratio of 2:1 replacement slum communities for stratum 1 or small size slum communities (i.e. 2 replacement slum communities for each slum community that needed to be selected). For stratum 2 (medium size slum communities), the first stage sampling included a ratio of 1:2 replacement slum communities (i.e. 1 replacement slum community for each 2 slum communities that needed to be selected). For stratum 3 (large size slum communities), the first stage sampling included a ratio of 1:3 replacement slum communities (i.e. 1 replacement slum community for each 3 slum communities that needed to be selected).

The listing and selection of households in the final stage also followed different field protocols across strata. For stratum 1 (small size slum communities), all households were listed. If the selected slum community had only 5 households, all households were interviewed. If the selected slum community had more than 5 households, systematic equal probability sampling was used based on the updated list to select 5 of them that would be selected to be interviewed. For stratum 2 (medium size slum communities), all households were listed and 10 were selected to be interviewed using systematic equal probability sampling based on the updated list. Lastly, for stratum 3, slum communities were segmented into listing blocks of around 200-250 slum households each. Then one of the segments was randomly selected from each slum community to conduct the full listing exercise. 10 households were selected from the updated list to be interviewed using systematic equal probability sampling.

The survey was fielded over a period of 16 days, using 30 teams of two interviewers each. Each team was responsible for visiting 2 slum communities during this period and administer 10 household surveys in each community. Slum communities were allocated into teams based on practical considerations (e.g. distance of slum communities). During the first two days, interviewers conducted the listing and did the sampling using the field protocols described above for each of the stratum. In the next 14 days, each enumerator visited 5 households each day for a total of 7 times to collect 2-days recall consumption data. At the end of the 14-days period, each enumerator was expected to have completed 10 full questionnaires (5 from each of assigned slum community).

Mode of data collection

Face-to-face [f2f]

Research instrument

One household level questionnaire

Cleaning operations

The CSPro data entry program was used for data entry and editing.

Thematic results at a glance.

Additional file 1. Calculation of Carcinogenic and Non-carcinogenic Health Risks due to the Inhalation of PM1.0 , PM2.5 and PM10.