Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference

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., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model outperforms state-of-the-art approaches

Urban air pollution modelling with machine learning using fixed and mobile sensors

Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution. The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018). The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces

On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

JUST OPEN A WINDOW: UNDERSTANDING THE VULNERABILITY TO SUMMER HEAT OF A MOUNTAIN COMMUNITY IN THE WESTERN UNITED STATES, MISSOULA, MT

How do we conceptualize vulnerability or resiliency to a natural hazard when it has not historically been understood as such? This study focuses on Missoula, located in mountains of western Montana, which has steadily grown by 1-2% per year to almost 75,000 residents. The formerly temperate quality of its winters and summers has also been changing. Projections from the 2017 Montana Climate Assessment estimate the state will experience a 2-5°F increase in mean annual air temperature over the next two decades, prompting city and county officials to plan for scenarios not formerly in their consideration. Of further concern is the increasing frequency of extensive summer wildfires and accompanying poor air quality that prevents the low cost venting of homes during cooler evenings. This study was facilitated by the American Geophysical Union’s Thriving Earth Exchange (TEX) collaboration between local (City of Missoula, Climate Smart Missoula), state (University of Montana), and national (TEX, University of Notre Dame) stakeholders seeking to create a climate change plan. Areal interpolation from U.S. Census American Community Survey block-group data to the block level, and dasymetric mapping were utilized to account for the unpopulated public lands that occupy substantial portions of many blocks. Socioeconomic variable layers (age, income, education, employment, living alone, multi-unit housing, mobile housing, insurance status, and disability) were combined in a Multi-Criteria Analysis to map sensitivity and exposure variables of land surface temperature and land-cover data to predict the populations most vulnerable to heat (and smoke) risks. The resulting maps will be utilized by Missoula city and county planners to allocate resources for mitigation, such as recommendations for the selection of building materials in new construction, installation of cooling shelters, and enhancement of urban forest. This study was designed to develop a methodology that could be readily replicated by other small communities to implement and update as needed

Intelligent Calibration and Virtual Sensing for Integrated Low-Cost Air Quality Sensors

This paper presents the development of air quality low-cost sensors (LCS) with improved accuracy features. The LCS features integrate machine learning based calibration models and virtual sensors. LCS performances are analyzed and some LCS variables with low performance are improved through intelligent field-calibrations. Meteorological variables are calibrated using linear dynamic models. While, due to the non-linear relationship to reference instruments, fine particulate matter (PM2.5) are calibrated using non-linear machine learning models. However, due to sensor drifts or faults, carbon dioxide (CO2) does not present correlation to reference instrument. As a result, the LCS for CO2 is not feasible to be calibrated. Hence, to estimate the CO2 concentration, mathematical models are developed to be integrated in the calibrated LCS, known as a virtual sensor. In addition, another virtual sensor is developed to demonstrate the capability of estimating air pollutant concentrations, e.g. black carbon, when the physical sensor devices are not available. In our paper, calibration models and virtual sensors are established using corresponding reference instruments that are installed on two reference stations. This strategy generalizes the models of calibration and virtual sensing which then allows LCS to be deployed in field independently with a high accuracy. Our proposed methodology enables scaling-up accurate air pollution mapping appropriate for smart cities.Peer reviewe

Retrieval of Multiple Atmospheric Environmental Parameters From Images With Deep Learning

Retrieving atmospheric environmental parameters such as atmospheric horizontal visibility and mass concentration of aerosol particles with a diameter of 2.5 or 10 μm or less (PM 2.5 , PM 10 , respectively) from digital images provides new tools for horizontal environmental monitoring. In this study, we propose a new end-to-end convolutional neural network (CNN) for the retrieval of multiple atmospheric environmental parameters (RMEPs) from images. In contrast to other retrieval models, RMEP can retrieve a suite of atmospheric environmental parameters including atmospheric horizontal visibility, relative humidity (RH), ambient temperature, PM 2.5 , and PM 10 simultaneously from a single image. Experimental results demonstrate that: 1) it is possible to simultaneously retrieve multiple atmospheric environmental parameters; 2) spatial and spectral resolutions of images are not the key factors for the retrieval on the horizontal scale; and 3) RMEP achieves the best overall retrieval performance compared with several classic CNNs such as AlexNet, ResNet-50, and DenseNet-121, and the results are based on experiments on images extracted from webcams located in different continents (test R2 values are 0.63, 0.72, and 0.82 for atmospheric horizontal visibility, RH, and ambient temperature, respectively). Experimental results show the potential of utilizing webcams to help monitor the environment. Code and more results are available at https://github.com/cvvsu/RMEP .Peer reviewe

Low-cost Air Quality Sensing Process: Validation by Indoor-Outdoor Measurements

Air pollution is a main challenge in societies with particulate matter PM2.5 as the major air pollutant causing serious health implications. Due to health and economic impacts of air pollution, low-cost and portable air quality sensors can be vastly deployed to gain personal air pollutant exposure. In this paper, we present an air quality sensing process needed for low-cost sensors which are planned for long-term use. The steps of this process include design and production, laboratory tests, field tests, deployment, and maintenance. As a case study we focus on the field test, where we use two generations of a portable air quality sensor (capable of measuring meteorological variables and PM2.5 to perform an indoor-outdoor measurement. The study found that all of the measurements shown to be consistent through validation among themselves. The sensors accuracy also demonstrate to be adequate by showing similar readings compared to the nearest air quality reference station.Peer reviewe