Rahman Mustafizur, Rashid Faijunnesa, Kumar Diwakar, Habib Md Ahosan, Ullah Ahmad
Department of Environmental Science and Disaster Management (ESDM), Noakhali Science and Technology University (NSTU), Noakhali 3814, Bangladesh.
Department of Quality, Environment, Health, and Safety (QEHS), United Engineering and Power Services Limited (UEPSL), United Group, United City, Madani Avenue, Dhaka 1212, Bangladesh.
Data Brief. 2024 Jun 6;55:110594. doi: 10.1016/j.dib.2024.110594. eCollection 2024 Aug.
This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM, PM) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM, APS-113BL for PM, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.
本研究提供了关于孟加拉国人口密集的达卡出口加工区（DEPZ）空气质量的宝贵数据集。它包括了颗粒物（PM，PM）和一氧化碳（CO）浓度数据集以及空气质量指数（AQI）值。2019年至2023年期间，使用用于PM的可吸入粉尘采样器APS - 113NL、用于PM的APS - 113BL以及用于测量CO浓度数据的路创AQ9901SD空气质量监测数据记录器，每月收集24小时的PM数据和8小时的CO数据。数据采样地点根据人口密度选定，并且还包括了DEPZ的就业数据，突出了人口密度可能上升的情况。本文还使用自回归移动平均（ARIMA）模型预测了污染物浓度、AQI值以及与空气污染物相关的健康危害。还使用均方根误差（RMSE）和平均绝对误差（MAE）来衡量ARIMA模型的性能。然而，这可用于提高公众对与空气污染相关的健康危害的认识，并鼓励他们采取措施减少接触空气污染物。此外，这些数据对于研究人员和政策制定者评估空气污染风险、制定控制策略以及改善DEPZ的空气质量可能会有帮助。
