Impacts of Air Pollution on Health and Cost of Illness in Jakarta, Indonesia

International Journal of Environmental Research and Public Health, Journal Year: 2023, Volume and Issue: 20(4), P. 2916 - 2916

Published: Feb. 7, 2023

(1) Background: This study aimed to quantify the health and economic impacts of air pollution in Jakarta Province, capital Indonesia. (2) Methods: We quantified burden fine particulate matter (PM2.5) ground-level Ozone (O3), which exceeds local global ambient quality standards. selected outcomes include adverse children, all-cause mortality, daily hospitalizations. used comparative risk assessment methods estimate burdens attributable PM2.5 O3, linking population data with relative risks from literature. The were calculated using cost-of-illness value statistical life-year approach. (3) Results: Our results suggest over 7000 10,000 deaths, 5000 hospitalizations that can be attributed each year Jakarta. annual total cost impact reached approximately USD 2943.42 million. (4) Conclusions: By assess Jakarta, our provides timely evidence needed prioritize clean actions taken promote public’s health.

Language: Английский

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Automatic robot Manoeuvres detection using computer vision and deep learning techniques: a perspective of internet of robotics things (IoRT)

Multimedia Tools and Applications, Journal Year: 2022, Volume and Issue: 82(15), P. 23251 - 23276

Published: Nov. 16, 2022

Language: Английский

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The effects of socioeconomic factors on particulate matter concentration in China's: New evidence from spatial econometric model

Journal of Cleaner Production, Journal Year: 2023, Volume and Issue: 417, P. 137969 - 137969

Published: July 3, 2023

Language: Английский

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Time Series Forecasting for Air Quality with Structured and Unstructured Data Using Artificial Neural Networks

Atmosphere, Journal Year: 2025, Volume and Issue: 16(3), P. 320 - 320

Published: March 11, 2025

Various machine learning algorithms exist to predict air quality, but they can only analyse structured data gathered from monitoring stations. However, the concentration of certain pollutants, such as PM2.5 and PM10, be visually significant when there is a marked difference in their levels. Consequently, quality meteorological cameras estimated integrated with stations generate an forecast. This research delves into prospect creating methodology capable rapidly processing this information producing precise predictions using time series analytics. paper presents study developing new model, “Convolutional Neural Network, Recurrent Network Dual Input Model” (CORD). model combines convolutional neural network (CNN) recurrent (RNN) models that are applied prediction create pollution-related forecasting function overcome stations’ physical limitations. CORD allows for dual input types: collected images (unstructured data) prototype could all indices worldwide, tested based on Air Quality Health Index provided by Hong Kong Observatory, unique data-analytic framework measurement. has similar result GRU slightly smaller mean absolute root square errors than LSTM. Compared ANN algorithm, better accuracy.

Language: Английский

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Predicting the Environmental Change of Carbon Emission Patterns in South Asia: A Deep Learning Approach Using BiLSTM

Atmosphere, Journal Year: 2022, Volume and Issue: 13(12), P. 2011 - 2011

Published: Nov. 30, 2022

China’s economy has made significant strides in the past three decades. As a direct result of “one belt, one road” (OBOR) initiative, country’s rate industrialization and urbanization is currently fastest entire world. This rapid development largely dependent on enormous amounts energy being consumed forms foundation world’s high levels carbon emissions. It generally agreed that production greenhouse gases, particularly dioxide, primary contributor to current state climate change. In this paper, CO2 emission prediction model based Bi-LSTM constructed. order conduct empirical tests model, study uses data from South Asian countries China 2001 2020. emissions 2022 2030 were predicted along with those other combined effects scientific technological progress, industrial structures, structure factors affecting When compared LSTM GRU methods, model’s results produced lower MAE, MSE, MAPE values, indicating it performs better. According findings, represent problem will become much worse future due India’s emissions, next 10 years, if government does not implement policies help reduce

Language: Английский

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Assessing the ambient air quality patterns associated to the COVID-19 outbreak in the Yangtze River Delta: A random forest approach

Chemosphere, Journal Year: 2022, Volume and Issue: 314, P. 137638 - 137638

Published: Dec. 21, 2022

Language: Английский

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Time-Series Forecasting of Seasonal Data Using Machine Learning Methods

Algorithms, Journal Year: 2023, Volume and Issue: 16(5), P. 248 - 248

Published: May 10, 2023

The models for forecasting time series with seasonal variability can be used to build automatic real-time control systems. For example, predicting the water flowing in a wastewater treatment plant calculate optimal electricity consumption. article describes performance analysis of various machine learning methods (SARIMA, Holt-Winters Exponential Smoothing, ETS, Facebook Prophet, XGBoost, and Long Short-Term Memory) data-preprocessing algorithms implemented Python. general methodology model building requirements input data sets are described. All use actual from sensors monitoring system. novelty this work is an approach that allows using limited history obtain predictions reasonable accuracy. made it possible achieve R-Squared accuracy more than 0.95. calculation minimized, which run algorithm embedded

Language: Английский

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A New Hybrid Forecasting Model Based on Dual Series Decomposition with Long-Term Short-Term Memory

International Journal of Intelligent Systems, Journal Year: 2023, Volume and Issue: 2023, P. 1 - 15

Published: June 22, 2023

In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air quality. Accurate and efficient prediction is of great significance to prevention control pollution. The quality monitoring network provides multisource concentration data for prediction, but based on still faces challenges each station’s series data. Aiming at problems low accuracy computational efficiency in traditional atmospheric using dual decomposition was proposed by variational mode (VMD), ensemble empirical (EEMD), long short-term memory (LSTM). First, historical Nanjing stations decomposed VMD, then EEMD algorithm applied residual VMD obtain several characteristic intrinsic function (IMF) components; IMF component trained LSTM result component, final can be obtained linear superposition. method achieved best results with R2 = 99%, MSE 5.38, MAE 4.54, MAPE 3.12. Because strong adaptive learning ability good function, it advantage long-term data, are more accurate. According superior baseline models terms statistical metrics. As a result, hybrid serve as reliable model forecasting.

Language: Английский

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A machine learning-based ensemble model for estimating diurnal variations of nitrogen oxide concentrations in Taiwan

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 916, P. 170209 - 170209

Published: Jan. 24, 2024

Language: Английский

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Advancing air quality forecasting in Abu Dhabi, UAE using time series models

Frontiers in Environmental Science, Journal Year: 2024, Volume and Issue: 12

Published: May 15, 2024

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive data collected from 2015 to 2023. We hourly nitrogen dioxide (NO2), particulate matter (PM10), and fine (PM2.5) 19 well-placed ground monitoring stations. Our approach utilized ARIMA forecast future pollutant levels, with extensive preparation exploratory analysis conducted R. results found a significant drop NO2 levels after 2020 the highest of observed 2022. The findings our confirm effectiveness models, indicated Mean Absolute Percentage Error (MAPE) values ranging 7.71 8.59. Additionally, study provides valuable spatiotemporal insights into pollution historical evolution, identifying key times areas heightened pollution, which can help devising focused management strategies. demonstrates potential precise forecasting, aiding proactive public health initiatives environmental policy development, consistent Dhabi’s Vision 2030.

Language: Английский

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