Site Suitability Analysis for Low Cost Sensor Networks for Urban Spatially Dense Air Pollution Monitoring

Advantages of Slow Sensing for Ambient Monitoring: A Practical Perspective

Air pollution is a ubiquitous threat, affecting 99% of the global populace and causing millions of premature deaths annually. Monitoring ambient air quality is essential, aiding policymakers and environmental agencies in timely interventions. This study delves into the advantages of slower gas sensors over their ultrafast counterparts, with a keen focus on their practicality in real-world scenarios. Slow sensors offer accurate time-averaged exposure assessments, harmonizing with established regulatory benchmarks. Their heightened precision and reliability, complemented by their cost-effectiveness, render them eminently suitable for large-scale deployment. The slow sensing ensures compatibility with regulations, fostering robust risk management practices. In contrast, ultrafast sensors, while claiming rapid detection, despite touting swift detection capabilities, grapple with formidable challenges. The sensitivity of ultrafast sensors to uncontrolled atmospheric effects, fluctuations in pressure, rapid response times, and uniform gas dispersion poses significant hurdles to their reliability. Addressing these issues assumes paramount significance in upholding the integrity of air quality assessments.

Spring 2020 Atmospheric Aerosol Contamination over Kyiv City

Extraordinarily high aerosol contamination was observed in the atmosphere over the city of Kyiv, Ukraine, during the March–April 2020 period. The source of contamination was the large grass and forest fires in the northern part of Ukraine and the Kyiv region. The level of PM2.5 load was investigated using newly established AirVisual sensor mini-networks in five areas of the city. The aerosol data from the Kyiv AERONET sun-photometer site were analyzed for that period. Aerosol optical depth, Ångström exponent, and the aerosol particles properties (particle size distribution, single-scattering albedo, and complex refractive index) were analyzed using AERONET sun-photometer observations. The smoke particles observed at Kyiv site during the fires in general correspond to aerosol with optical properties of biomass burning aerosol. The variability of the optical properties and chemical composition indicates that the aerosol particles in the smoke plumes over Kyiv city were produced by different burning materials and phases of vegetation fires at different times. The case of enormous PM2.5 aerosol contamination in the Kyiv city reveals the need to implement strong measures for forest fire control and prevention in the Kyiv region, especially in its northwest part, where radioactive contamination from the Chernobyl disaster is still significant.

Sensitivity Operator Framework for Analyzing Heterogeneous Air Quality Monitoring Systems

Air quality monitoring systems differ in composition and accuracy of observations and their temporal and spatial coverage. A monitoring system’s performance can be assessed by evaluating the accuracy of the emission sources identified by its data. In the considered inverse modeling approach, a source identification problem is transformed to a quasi-linear operator equation with the sensitivity operator. The sensitivity operator is composed of the sensitivity functions evaluated on the adjoint ensemble members. The members correspond to the measurement data element aggregates. Such ensemble construction allows working in a unified way with heterogeneous measurement data in a single-operator equation. The quasi-linear structure of the resulting operator equation allows both solving and predicting solutions of the inverse problem. Numerical experiments for the Baikal region scenario were carried out to compare different types of inverse problem solution accuracy estimates. In the considered scenario, the projection to the orthogonal complement of the sensitivity operator’s kernel allowed predicting the source identification results with the best accuracy compared to the other estimate types. Our contribution is the development and testing of a sensitivity-operator-based set of tools for analyzing heterogeneous air quality monitoring systems. We propose them for assessing and optimizing observational systems and experiments.

Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

The Historical Trend of Air Pollution and Its Impact on Human Health in Campania Region (Italy)

The Campania region covers an area of about 13,590 km2 with 5.8 million residents. The area suffers from several environmental issues due to urbanization, the presence of industries, wastewater treatment, and solid waste management concerns. Air pollution is one of the most relevant environmental troubles in the Campania region, frequently exceeding the limit values established by European directives. In this paper, airborne pollutant concentration data measured by the regional air quality network from 2003 to 2019 are collected to individuate the historical trends of nitrogen dioxide (NO2), coarse and fine particulate matter with aerodynamic diameters smaller than 10 μm (PM10) and 2.5 μm (PM2.5), and ozone (O3) through the analysis of the number of exceedances of limit values per year and the annual average concentration. Information on spatial variability and the effect of the receptor category is obtained by lumping together data belonging to the same province or category. To obtain information on the general air quality rather than on single pollutants, the European Air Quality Index (EU-AQI) is also evaluated. A special focus is dedicated to the effect of deep street canyons on air quality, since they are very common in the urban areas in Campania. Finally, the impact of air pollution from 2003 to 2019 on human health is also analyzed using the software AIRQ+.