4.7 Article

Integrating low-cost sensor monitoring, satellite mapping, and geospatial artificial intelligence for intra-urban air pollution predictions

Related references

Note: Only part of the references are listed.
Article Engineering, Environmental

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Jianzhao Bi et al.

Summary: This study develops a PM2.5 forecast framework by combining the Random Forest algorithm with NASA's GEOS-CF forecast product, providing continuous PM2.5 concentration forecasts for the next 5 days. The experiment conducted in a region in Central China shows high accuracy of the forecast results and a reduction in biases compared to GEOS-CF.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

Article Engineering, Environmental

Data-Driven Machine Learning in Environmental Pollution: Gains and Problems

Xian Liu et al.

Summary: The complexity and dynamics of the environment pose challenges for predicting and tracing pollution changes. However, the development of machine learning methods provides opportunities for environmental pollution research. The use of machine learning in environmental process studies still has limitations, and its application in areas other than air pollution needs to be increased.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2022)

Article Multidisciplinary Sciences

Air pollution exposure disparities across US population and income groups

Abdulrahman Jbaily et al.

Summary: Air pollution, particularly exposure to fine particulate matter (PM2.5), contributes significantly to global mortality rates. In the United States, racial/ethnic minorities and lower-income groups face a higher risk of death from PM2.5 exposure. Additionally, disparities in air pollution exposure exist among different population and income groups. Regions with higher proportions of White and Native American populations consistently experience lower PM2.5 levels compared to regions with higher proportions of Black, Asian, Hispanic, or Latino populations. Low-income areas consistently have higher average PM2.5 levels than high-income areas. Moreover, exposure disparities relative to safety standards set by the US Environmental Protection Agency and World Health Organization have increased over time. These findings highlight the need for targeted measures to reduce PM2.5 and ensure equitable environmental protection. This study is observational and cannot provide insights into the driving factors behind the identified disparities.

NATURE (2022)

Article Environmental Sciences

Community-based participatory research for low-cost air pollution monitoring in the wake of unconventional oil and gas development in the Ohio River Valley: Empowering impacted residents through community science

Garima Raheja et al.

Summary: Residents in Belmont County, Ohio are using participatory science to study the impact of unconventional oil and gas development on their community's health. They found that the government monitoring network is inadequate and are working with the EPA to develop better air quality standards. This study also serves as a platform for future community science efforts.

ENVIRONMENTAL RESEARCH LETTERS (2022)

Article Multidisciplinary Sciences

Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset

Chuizheng Meng et al.

Summary: The recent release of large-scale healthcare datasets has accelerated the research on data-driven deep learning models in healthcare applications. This study focuses on analyzing the interpretability, dataset representation bias, and prediction fairness of deep learning models on the largest publicly available healthcare dataset, MIMIC-IV. The findings reveal that prediction performance is not the sole factor to consider when evaluating healthcare models, as high performance may be achieved through unfair utilization of demographic features. The study suggests that future research should incorporate interpretability and fairness analysis to ensure superior performance without introducing bias.

SCIENTIFIC REPORTS (2022)

Article Chemistry, Multidisciplinary

Collaborative Approach between Explainable Artificial Intelligence and Simplified Chemical Interactions to Explore Active Ligands for Cyclin-Dependent Kinase 2

Tomomi Shimazaki et al.

Summary: This study proposes a collaborative approach using explainable artificial intelligence and simplified chemical interaction scores to efficiently search for active ligands bound to the target receptor in drug discovery virtual screening. By employing machine learning and simplified scoring functions, the research demonstrates improved classification ability of machine learning models and highlights important residues of the target receptor.

ACS OMEGA (2022)

Article Environmental Sciences

Synergistic data fusion of satellite observations and in-situ measurements for hourly PM2.5 estimation based on hierarchical geospatial long short-term memory

Xinyu Yu et al.

Summary: This study proposes a hierarchical geospatial long short-term memory method (HG-LSTM) for PM2.5 estimation with 2-km spatial resolution in the Yangtze River Delta urban agglomeration. The results show that the HG-LSTM model accurately estimates hourly PM2.5 concentrations and reveals the spatiotemporal characteristics of PM2.5 in the study area.

ATMOSPHERIC ENVIRONMENT (2022)

Article Environmental Sciences

UrbanWatch: A 1-meter resolution land cover and land use database for 22 major cities in the United States

Yindan Zhang et al.

Summary: In this study, a framework called FLUTE was developed to address challenges in large-area, high-resolution urban mapping. By utilizing a teacher-student deep learning architecture and new feature extraction modules, FLUTE successfully created a high-accuracy land cover and land use database, which can support urban-related research and planning.

REMOTE SENSING OF ENVIRONMENT (2022)

Article Environmental Sciences

Urban edge trees: Urban form and meteorology drive elemental carbon deposition to canopies and soils

Alexandra G. Ponette-Gonzalez et al.

Summary: Urban tree canopies play an important role in absorbing atmospheric elemental carbon (EC). The deposition of EC on trees is influenced by urban form characteristics, meteorological factors, and topography. The study found that urban edge trees significantly contribute to dry EC deposition, while rainfall and wind-driven rain from pollution sources drive throughfall EC deposition.

ENVIRONMENTAL POLLUTION (2022)

Article Environmental Sciences

What Influences Low-cost Sensor Data Calibration? - A Systematic Assessment of Algorithms, Duration, and Predictor Selection

Lu Liang et al.

Summary: The low-cost sensor has revolutionized air quality monitoring, leading to increased interest in understanding its data quality. This study comprehensively assessed ten popular calibration techniques and found that the neural network achieved the best performance. Regression methods also showed consistent and stable performance. The sample size effect was evident when the sample size dropped below 30%, and adding more predictors generally improved algorithm performance.

AEROSOL AND AIR QUALITY RESEARCH (2022)

Article Environmental Sciences

Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018

Qingqing He et al.

ENVIRONMENT INTERNATIONAL (2021)

Article Engineering, Environmental

National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment

Tianjun Lu et al.

Summary: This study investigates the use of microscale variables to improve prediction accuracy in air pollution models, and finds that models combining microscale and traditional predictor variables outperform traditional methods. Microscale variables have potential as suitable substitutes for traditional variables in national empirical models.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

Article Environmental Sciences

Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration

Huang Zhang et al.

Summary: A new interpolation algorithm, Win-OK, was developed to predict the spatial distribution of ground-level PM2.5 by accounting for wind direction. The performance of Win-OK was found to be more stable and accurate compared to the traditional method OK when analyzing PM2.5 concentration heat-maps.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

Article Engineering, Chemical

From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors

Michael R. Giordano et al.

Summary: Low-cost sensors for particulate matter mass (PM) provide spatially dense, high temporal resolution measurements of air quality, especially beneficial in low and middle-income countries with limited reference grade measurements. However, these sensors also face challenges that must be addressed to ensure data quality.

JOURNAL OF AEROSOL SCIENCE (2021)

Article Environmental Sciences

Constructing a spatiotemporally coherent long-term PM2.5 concentration dataset over China during 1980-2019 using a machine learning approach

Huimin Li et al.

Summary: This study constructed a dataset of near-surface PM2.5 concentrations in China from 1980 to 2019 using a space-time randomforest model, with simulated concentrations showing excellent agreement with ground measurements. Atmospheric visibility, emissions, and meteorological conditions were identified as key factors affecting PM2.5 predictions. Clean air actions have effectively reduced PM2.5 concentrations in certain regions of China.

SCIENCE OF THE TOTAL ENVIRONMENT (2021)

Review Environmental Sciences

Calibrating low-cost sensors for ambient air monitoring: Techniques, trends, and challenges

Lu Liang

Summary: The global sensor market is rapidly expanding due to surging needs, but calibration efforts have been focused on a limited selection of sensors. Relative humidity correction, regression, and machine learning are the mainstream calibration techniques. Machine learning is a key trend in calibration, but issues such as calibration duration and spatial mismatch still need to be addressed.

ENVIRONMENTAL RESEARCH (2021)

Article Engineering, Environmental

Data Analytics for Environmental Science and Engineering Research

Suraj Gupta et al.

Summary: The advancement of new data acquisition and handling techniques has enabled researchers to use machine learning techniques for analyzing complex environmental systems, leading to more comprehensive approaches in environmental monitoring. The current applications of ML algorithms in Environmental Science and Engineering include metagenomic data analysis for antimicrobial resistance, nontarget analysis for environmental pollutant profiling, and anomaly detection in continuous data from engineered water systems.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2021)

Article Meteorology & Atmospheric Sciences

Development and application of a United States-wide correction for PM2.5 data collected with the PurpleAir sensor

Karoline K. Barkjohn et al.

Summary: PurpleAir sensors, widely used for measuring particulate matter, have been evaluated and compared with ambient air quality regulatory instruments, resulting in a correction of sensor data biases.

ATMOSPHERIC MEASUREMENT TECHNIQUES (2021)

Article Engineering, Environmental

Incorporating Low-Cost Sensor Measurements into High-Resolution PM2.5 Modeling at a Large Spatial Scale

Jianzhao Bi et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2020)

Article Environmental Sciences

Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling

Lianfa Li et al.

REMOTE SENSING OF ENVIRONMENT (2020)

Article Nanoscience & Nanotechnology

Elucidating the Behavior of Nanophotonic Structures through Explainable Machine Learning Algorithms

Christopher Yeung et al.

ACS PHOTONICS (2020)

Article Meteorology & Atmospheric Sciences

Evaluation, Tuning, and Interpretation of Neural Networks for Working with Images in Meteorological Applications

Imme Ebert-Uphoff et al.

BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY (2020)

Article Multidisciplinary Sciences

Disparities in PM2.5 air pollution in the United States

Jonathan Colmer et al.

SCIENCE (2020)

Article Computer Science, Information Systems

Explainable Machine Learning for Scientific Insights and Discoveries

Ribana Roscher et al.

IEEE ACCESS (2020)

Article Public, Environmental & Occupational Health

Assessment of personal exposure to particulate air pollution: the first result of City Health Outlook (CHO) project

Lu Liang et al.

BMC PUBLIC HEALTH (2019)

Article Engineering, Environmental

A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California

Julien J. Caubel et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2019)

Article Chemistry, Multidisciplinary

Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

David E. Williams

ACS SENSORS (2019)

Review Geosciences, Multidisciplinary

Low-Cost Environmental Sensor Networks: Recent Advances and Future Directions

Feng Mao et al.

FRONTIERS IN EARTH SCIENCE (2019)

Article Engineering, Environmental

Performance of Prediction Algorithms for Modeling Outdoor Air Pollution Spatial Surfaces

Jules Kerckhoffs et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2019)

Article Computer Science, Artificial Intelligence

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin

NATURE MACHINE INTELLIGENCE (2019)

Article Public, Environmental & Occupational Health

Disparities in Distribution of Particulate Matter Emission Sources by Race and Poverty Status

Ihab Mikati et al.

AMERICAN JOURNAL OF PUBLIC HEALTH (2018)

Review Medicine, General & Internal

The Lancet Commission on pollution and health

Philip J. Landrigan et al.

LANCET (2018)

Article Environmental Sciences

Predicting daily PM2.5 concentrations in Texas using high-resolution satellite aerosol optical depth

Xueying Zhang et al.

SCIENCE OF THE TOTAL ENVIRONMENT (2018)

Article Meteorology & Atmospheric Sciences

Spatial Representativeness of PM2.5 Concentrations Obtained Using Observations From Network Stations

Xiaoqin Shi et al.

JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES (2018)

Article Environmental Sciences

Airborne particulate matter monitoring in Kenya using calibrated low-cost sensors

Francis D. Pope et al.

ATMOSPHERIC CHEMISTRY AND PHYSICS (2018)

Article Meteorology & Atmospheric Sciences

MODIS Collection 6 MAIAC algorithm

Alexei Lyapustin et al.

ATMOSPHERIC MEASUREMENT TECHNIQUES (2018)

Article Engineering, Environmental

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data

Joshua S. Apte et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2017)

Article Engineering, Electrical & Electronic

Comparison of Canopy Cover Estimations From Airborne LiDAR, Aerial Imagery, and Satellite Imagery

Qin Ma et al.

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (2017)

Article Environmental Sciences

Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China

Qingyang Xiao et al.

REMOTE SENSING OF ENVIRONMENT (2017)

Article Environmental Sciences

Using spatio-temporal land use regression models to address spatial variation in air pollution concentrations in time series studies

Konstantina Dimakopoulou et al.

AIR QUALITY ATMOSPHERE AND HEALTH (2017)

Article Engineering, Environmental

Spatiotemporal Prediction of Fine Particulate Matter During the 2008 Northern California Wildfires Using Machine Learning

Colleen E. Reid et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2015)

Article Engineering, Environmental

Using High-Resolution Satellite Aerosol Optical Depth To Estimate Daily PM2.5 Geographical Distribution in Mexico City

Allan C. Just et al.

ENVIRONMENTAL SCIENCE & TECHNOLOGY (2015)

Article Health Policy & Services

Purposeful Sampling for Qualitative Data Collection and Analysis in Mixed Method Implementation Research

Lawrence A. Palinkas et al.

ADMINISTRATION AND POLICY IN MENTAL HEALTH AND MENTAL HEALTH SERVICES RESEARCH (2015)

Article Environmental Sciences

Monitoring intraurban spatial patterns of multiple combustion air pollutants in New York City: Design and implementation

Thomas D. Matte et al.

JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY (2013)

Review Environmental Sciences

Using Inequality Measures to Incorporate Environmental Justice into Regulatory Analyses

Sam Harper et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2013)

Article Environmental Sciences

Environmental Inequality in Exposures to Airborne Particulate Matter Components in the United States

Michelle L. Bell et al.

ENVIRONMENTAL HEALTH PERSPECTIVES (2012)

Article Computer Science, Interdisciplinary Applications

A cokriging based approach to reconstruct air pollution maps, processing measurement station concentrations and deterministic model simulations

Vikas Singh et al.

ENVIRONMENTAL MODELLING & SOFTWARE (2011)

Article Environmental Sciences

Making the Environmental Justice Grade: The Relative Burden of Air Pollution Exposure in the United States

Marie Lynn Miranda et al.

INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2011)