1
|
Gupta P, Ferrer-Cid P, Barcelo-Ordinas JM, Garcia-Vidal J, Soni VK, Pöhlker ML, Ahlawat A, Viana M. Estimating black carbon levels using machine learning models in high-concentration regions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 948:174804. [PMID: 39019282 DOI: 10.1016/j.scitotenv.2024.174804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/25/2024] [Accepted: 07/12/2024] [Indexed: 07/19/2024]
Abstract
Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.
Collapse
Affiliation(s)
- Pratima Gupta
- Centre for Atmospheric Sciences, Indian Institute of Technology (IIT) Delhi, India
| | - Pau Ferrer-Cid
- Department of Computer Architecture, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Jose M Barcelo-Ordinas
- Department of Computer Architecture, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | - Jorge Garcia-Vidal
- Department of Computer Architecture, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
| | | | - Mira L Pöhlker
- Atmospheric Microphysics Department, Leibniz Institute for Tropospheric Research, Leipzig, Germany
| | - Ajit Ahlawat
- Atmospheric Microphysics Department, Leibniz Institute for Tropospheric Research, Leipzig, Germany.
| | - Mar Viana
- Institute of Environmental Assessment and Water Research, Spanish Research Council, IDAEA-CSIC, Barcelona, Spain
| |
Collapse
|
2
|
Mirzavand Borujeni S, Arras L, Srinivasan V, Samek W. Explainable sequence-to-sequence GRU neural network for pollution forecasting. Sci Rep 2023; 13:9940. [PMID: 37336995 DOI: 10.1038/s41598-023-35963-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 05/26/2023] [Indexed: 06/21/2023] Open
Abstract
The goal of pollution forecasting models is to allow the prediction and control of the air quality. Non-linear data-driven approaches based on deep neural networks have been increasingly used in such contexts showing significant improvements w.r.t. more conventional approaches like regression models and mechanistic approaches. While such deep learning models were deemed for a long time as black boxes, recent advances in eXplainable AI (XAI) allow to look through the model's decision-making process, providing insights into decisive input features responsible for the model's prediction. One XAI technique to explain the predictions of neural networks which was proven useful in various domains is Layer-wise Relevance Propagation (LRP). In this work, we extend the LRP technique to a sequence-to-sequence neural network model with GRU layers. The explanation heatmaps provided by LRP allow us to identify important meteorological and temporal features responsible for the accumulation of four major pollutants in the air ([Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text]), and our findings can be backed up with prior knowledge in environmental and pollution research. This illustrates the appropriateness of XAI for understanding pollution forecastings and opens up new avenues for controlling and mitigating the pollutants' load in the air.
Collapse
Affiliation(s)
- Sara Mirzavand Borujeni
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany
| | - Leila Arras
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, 10587, Berlin, Germany
| | - Vignesh Srinivasan
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany
| | - Wojciech Samek
- Department of Artificial Intelligence, Fraunhofer Heinrich Hertz Institute, 10587, Berlin, Germany.
- Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587, Berlin, Germany.
- BIFOLD-Berlin Institute for the Foundations of Learning and Data, 10587, Berlin, Germany.
| |
Collapse
|
3
|
Wang C, Guo M, Jin J, Yang Y, Ren Y, Wang Y, Cao J. Does the Spatial Pattern of Plants and Green Space Affect Air Pollutant Concentrations? Evidence from 37 Garden Cities in China. PLANTS (BASEL, SWITZERLAND) 2022; 11:2847. [PMID: 36365300 PMCID: PMC9655052 DOI: 10.3390/plants11212847] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/09/2022] [Accepted: 10/24/2022] [Indexed: 06/16/2023]
Abstract
Relevant studies have demonstrated that urban green spaces composed of various types of plants are able to alleviate the morbidity and mortality of respiratory diseases, by reducing air pollution levels. In order to explore the relationship between the spatial pattern of urban green spaces and air pollutant concentrations, this study takes 37 garden cities with subtropical monsoon climate in China as the research object and selects the urban air quality monitoring data and land use type data in 2019 to analyze the relationship between the spatial pattern and the air pollutant concentration through the landscape metrics model and spatial regression model. Moreover, the threshold effect of the impact of green space on air pollutant concentrations is estimated, as well. The results showed that the spatial pattern of urban green space was significantly correlated with the concentrations of PM2.5 (PM with aerodynamic diameters of 2.5 mmor less), NO2 (Nitrogen Dioxide), and SO2 (Sulfur dioxide) pollutants in the air, while the concentrations of PM10 (PM with aerodynamic diameters of 10 mmor less) pollutants were not significantly affected by the green space pattern. Among them, the patch shape index (LSI), patch density (PD) and patch proportion in landscape area (PLAND) of forest land can affect the concentration of PM2.5, NO2, and SO2, respectively. The PLAND, PD, and LSI of grassland and farmland can also have an additional impact on the concentration of SO2 pollutants. The study also found that there was a significant threshold effect within the impact mechanism of urban green space landscape pattern indicators (LSI, PD, PLAND) on the concentrations of PM2.5, NO2, and SO2 air pollutants. The results of this study not only clarified the impact mechanism of the spatial pattern of urban green space on air pollutant concentrations but also provided quantitative reference and scientific basis for the optimization and updating of urban green space to promote public health.
Collapse
Affiliation(s)
- Chengkang Wang
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Mengyue Guo
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Jun Jin
- Research Institute of Architecture, Southeast University, Nanjing 210096, China
| | - Yifan Yang
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| | - Yujie Ren
- Graduate School of Human-Environment Studies, Kyushu University, Fukuoka 819-0395, Japan
| | - Yang Wang
- State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Jiajie Cao
- College of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
| |
Collapse
|
4
|
Relationship between Land-Use Type and Daily Concentration and Variability of PM10 in Metropolitan Cities: Evidence from South Korea. LAND 2021. [DOI: 10.3390/land11010023] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Since urban areas with high air pollution are known to have higher mortality rates compared to areas with less air pollution, accurately understanding and predicting the distribution of particulate matter (PM) in cities is important for urban planning policies that seek to emphasize the health of citizens. Therefore, this study aims to investigate the relationship between PM and land use in metropolitan cities in South Korea using the land-use regression model. We use daily data from the air quality monitoring stations (AQMS) in seven cities in South Korea for the year 2018. For analysis, K-means clustering is employed to identify the land-use pattern surrounding the AQMSs and two log-lin regression models are used to investigate the effects of each land-use type on PM. The findings show a statistically significant difference in PM concentration and variability in the business, commercial, industrial, mixed, and high-density residential areas compared to parks and green areas, and that PM concentration and variability were less in mixed areas than in single land use, thus verifying the effectiveness of a mixed land-use planning strategy. Moreover, microclimatic, seasonal, and regional factors affect PM concentration and variability. Finally, to minimize exposure to PM, various policies such as mixed land use need to be established and implemented differently, depending on the season and time.
Collapse
|
5
|
Wang S, Li P, Ji H, Zhan Y, Li H. Prediction of air particulate matter in Beijing, China, based on the improved particle swarm optimization algorithm and long short-term memory neural network. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Intelligent algorithms using deep learning can help learn feature data with nonlinearity and uncertainty, such as time-series particle concentration data. This paper proposes an improved particle swarm optimization (IPSO) algorithm using nonlinear decreasing weights to optimize the hyperparameters, such as the number of hidden layer neurons, learning rate, and maximum number of iterations of the long short-term memory (LSTM) neural network, to predict the time series for air particulate concentration and capture its data dependence. The IPSO algorithm uses nonlinear decreasing weights to make the inertia weights nonlinearly decreasing during the iteration process to improve the convergence speed and capability of finding the global optimization of the PSO. This study addresses the limitations of the traditional method and exhibits accurate predictions. The results of the improved algorithm reveal that the root means square, mean absolute percentage error, and mean absolute error of the IPSO-LSTM model predicted changes in six particle concentrations, which decreased by 1.59% to 5.35%, 0.25% to 3.82%, 7.82% to 13.65%, 0.7% to 3.62%, 0.01% to 3.55%, and 1.06% to 17.21%, respectively, compared with the LSTM and PSO-LSTM models. The IPSO-LSTM prediction model has higher accuracy than the other models, and its accurate prediction model is suitable for regional air quality management and effective control of the adverse effects of air pollution.
Collapse
Affiliation(s)
- Shengwei Wang
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
| | - Ping Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
| | - Hao Ji
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
| | - Yulin Zhan
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
| | - Honghong Li
- College of Computer Science and Engineering, Northwest Normal University, Lanzhou, China
| |
Collapse
|
6
|
Shatnawi N, Abu-Qdais H. Assessing and predicting air quality in northern Jordan during the lockdown due to the COVID-19 virus pandemic using artificial neural network. AIR QUALITY, ATMOSPHERE, & HEALTH 2021; 14:643-652. [PMID: 33520010 PMCID: PMC7831622 DOI: 10.1007/s11869-020-00968-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Accepted: 11/25/2020] [Indexed: 05/16/2023]
Abstract
This study deals with the simulation and prediction of air pollutants in Irbid city (north of Jordan) before and during the spread of the COVID-19 virus pandemic by using an artificial neural network (ANN). Based on the data obtained from the air quality monitoring station for the year 2019 and the first quarter of the year 2020, it was possible to develop an ANN model to simulate and predict the concentrations of three air pollutants, namely nitrogen dioxide (NO2), sulfur dioxide (SO2), and particulate matter with diameter less than 10 μm (PM10). Several ANN model configurations were tested to select the best model that could predict the concentration of the three air pollutants with meteorological parameters being used as input to the model. The results showed that the concentration of the pollutants during the coronavirus lockdown was declined by various percentages (from 29% for PM10 to 72% for NO2) as compared to their concentration before the pandemic period. Furthermore, the developed ANN model could simulate and predict the concentration of the pollutants during the pandemic period with sufficient accuracy as judged by the values of the coefficient of determination and the mean square error. The study results indicate that properly trained and structured ANN can be a useful tool to predict air quality parameters with adequate accuracy.
Collapse
Affiliation(s)
- Nawras Shatnawi
- Surveying and Geomatics Engineering Department, Al-Balqa Applied University, Al-Salt, 19117 Jordan
| | - Hani Abu-Qdais
- Civil Engineering Department, Jordan University of Science and Technology, P.O. Box 3030, Irbid, 22110 Jordan
| |
Collapse
|
7
|
Cabaneros SM, Calautit JK, Hughes B. Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique. Ecol Modell 2020. [DOI: 10.1016/j.ecolmodel.2020.109017] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
|
8
|
Araki S, Shima M, Yamamoto K. Spatiotemporal land use random forest model for estimating metropolitan NO 2 exposure in Japan. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 634:1269-1277. [PMID: 29710628 DOI: 10.1016/j.scitotenv.2018.03.324] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2018] [Revised: 03/06/2018] [Accepted: 03/26/2018] [Indexed: 05/06/2023]
Abstract
Adequate spatial and temporal estimates of NO2 concentrations are essential for proper prenatal exposure assessment. Here, we develop a spatiotemporal land use random forest (LURF) model of the monthly mean NO2 over four years in a metropolitan area of Japan. The overall objective is to obtain accurate NO2 estimates for use in prenatal exposure assessments. We use random forests to convey the non-linear relationship between NO2 concentrations and predictor variables, and compare the prediction accuracy with that of a linear regression. In addition, we include the distance decay effect of emission sources on NO2 concentrations for more efficient model construction. The prediction accuracy of the LURF model is evaluated through a leave-one-monitor-out cross validation. We obtain a high R2 value of 0.79, which is better than that of the conventional land use regression model using linear regression (R2 of 0.73). We also evaluate the LURF model via a temporal and overall cross validation and obtain R2 values of 0.84 and 0.92, respectively. We successfully integrate temporal and spatial components into our model, which exhibits higher accuracy than spatial models constructed individually for each month. Our findings illustrate the advantage of using a LURF to model the spatiotemporal variability of NO2 concentrations.
Collapse
Affiliation(s)
- Shin Araki
- Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka 565-0871, Japan.
| | - Masayuki Shima
- Department of Public Health, Hyogo College of Medicine, Mukogawa-cho 1-1, Nishinomiya, Hyogo 663-8501, Japan.
| | - Kouhei Yamamoto
- Graduate School of Energy Science, Kyoto University, Yoshidahonmachi, Sakyo, Kyoto 606-8501, Japan.
| |
Collapse
|
9
|
Cabaneros SMS, Calautit JKS, Hughes BR. Hybrid Artificial Neural Network Models for Effective Prediction and Mitigation of Urban Roadside NO 2 Pollution. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.egypro.2017.12.240] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
10
|
Statistical Modeling Approaches for PM10 Prediction in Urban Areas; A Review of 21st-Century Studies. ATMOSPHERE 2016. [DOI: 10.3390/atmos7020015] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|