arXiv research

GraphSVR forecasts urban air pollution robustly across stations and seasons.

Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…

Recently, air pollution is one of the most concerns for big cities. Predicting air quality for any regions and at any time is a critical requirement of urban citizens. However, air pollution prediction for the whole city is a challenging problem. The reason is, there are many spatiotemporal factors affecting air pollut…

AirRL uses RL to infer urban air quality from selected stations.

Study shows reducing anthropogenic emissions significantly lowers $PM_{2.5}$ levels but has little effect on $O_3$ in Delhi.

Tackling air pollution is an imperative problem in South Korea, especially in urban areas, over the last few years. More specially, South Korea has joined the ranks of the world's most polluted countries alongside with other Asian capitals, such as Beijing or Delhi. Much research is being conducted in environmental sci…

Interpretable additive models outperform complex DL and hybrid pipelines for air quality forecasting.

Work addresses long-term accuracy issues in IoT air quality sensors.

This paper analyzes air pollution trends in Rwanda using low-cost sensors and machine learning.

Quantile gradient boosted trees outperform other models in predicting NO2 concentration distributions.

DGPs improve air quality inference from sparse data.

High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, m…

Researchers develop a new method to assess variable importance in spatial machine learning models for air pollution exposure prediction.

Paper uses Gaussian Processes to monitor air quality in Kampala.

Weather2vec learns representations to adjust for non-local confounding in air pollution studies.

China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…

Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of seve…

The dynamic nature of air quality chemistry and transport makes it difficult to identify the mixture of air pollutants for a region. In this study of air quality in the Houston metropolitan area we apply dynamic principal component analysis (DPCA) to a normalized multivariate time series of daily concentration measurem…

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., m…

Artificial Neural Network predicts PM2.5 pollution with low-cost sensors.

The study corrects measurement error in evaluating health effects of multiple pollutants.

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this paper, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality rel…

Because of the prominent position of urban rail in reducing urban transport-related problems, such as congestion and air pollution, insights into the costs of possible new urban rail projects is very relevant for those involved with cost estimations, policy makers, cost-benefit analysts, and other target groups. Knowle…

Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of P…

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence e…

Study improves PM concentration forecasting using MCCR loss.

Bayesian method estimates Kronecker graphical models from autoregressive processes.

Study characterizes PM2.5 dynamics in Bujumbura using low-cost sensors.

MapLUR uses deep learning on map images to estimate NO2 pollution, outperforming traditional methods.

Predicts local AQI using mobile sensor data, improving accuracy by 71.654 MSE.

Modeling air pollutants using data-driven techniques and sparse identification of nonlinear dynamics.

Data collection in economically constrained countries often necessitates using approximate and biased measurements due to the low-cost of the sensors used. This leads to potentially invalid predictions and poor policies or decision making. This is especially an issue if methods from resource-rich regions are applied wi…

DCK improves air quality index prediction with probabilistic spatial models.

The economic and social impact of poor air quality in towns and cities is increasingly being recognised, together with the need for effective ways of creating awareness of real-time air quality levels and their impact on human health. With local authority maintained monitoring stations being geographically sparse and t…

RESPIRE calibrates low-cost air-quality sensors for CO levels, resistant to outliers.

Across numerous applications, forecasting relies on numerical solvers for partial differential equations (PDEs). Although the use of deep-learning techniques has been proposed, actual applications have been restricted by the fact the training data are obtained using traditional PDE solvers. Thereby, the uses of deep-le…

Engine predicts real-time air quality with high resolution.

Engine forecasts NO2, O3, PM2.5, PM10 with high accuracy.

Bayesian network framework assesses urban risks across multiple domains.

Double machine learning improves causal effect estimation by relaxing assumptions.

CRE discovers interpretable subgroups with heterogeneous treatment effects.

This paper establishes that so-called instrumental variables enable the identification and the estimation of a fully nonparametric regression model with Berkson-type measurement error in the regressors. An estimator is proposed and proven to be consistent. Its practical performance and feasibility are investigated via …

Framework detects shape shifts in functional profiles using Fréchet mean and shape invariant model.

Study compares geostatistical and machine learning models for PM2.5 prediction.

Reduced-order model improves LES for atmospheric pollutant dispersion.

Study uses open data to improve traffic emissions estimation.

We consider evidence integration from potentially dependent observation processes under varying spatio-temporal sampling resolutions and noise levels. We develop a multi-resolution multi-task (MRGP) framework while allowing for both inter-task and intra-task multi-resolution and multi-fidelity. We develop shallow Gauss…

In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, …