Search

Low-cost air quality sensors have the potential to complement the regulatory network of air quality monitoring stations, with respect to increased spatial density of observations, however, their data quality continues to be of concern. Here we report on our experience with a small network of open low-cost sensor systems for air quality, which was deployed in the region of Stavanger, Norway, under Nordic winter conditions. The network consisted of AirSensEUR sensor systems, equipped with sensors for, among others, nitrogen dioxide and fine particulate matter. The systems were co-located at an air quality monitoring station, for a period of approximately six weeks. A subset of the systems was subsequently deployed at various roadside locations for half a year, and finally co-located at the same air quality monitoring station again, for a post-deployment evaluation. For fine particulate matter, the co-location results indicate a good inter-unit consistency, but poor average out-of-the-box performance (R2 = 0.25, RMSE = 9.6 μ g m − 3 ). While Köhler correction did not significantly improve the accuracy in our study, filtering for high relative humidity conditions improved the results (R2 = 0.63, RMSE = 7.09 μ g m − 3 ). For nitrogen dioxide, the inter-unit consistency was found to be excellent, and calibration models were developed which showed good performance during the testing period (on average R2 = 0.98, RMSE = 5.73 μ g m − 3 ), however, due to the short training period, the calibration models are likely not able to capture the full annual variability in environmental conditions. A post-deployment co-location showed, respectively, a slight and significant decrease in inter-sensor consistency for fine particulate matter and nitrogen dioxide. We further demonstrate, how observations from even such a small network can be exploited by assimilation in a high-resolution air quality model, thus adding value to both the observations and the model, and ultimately providing a more comprehensive perspective of air quality than is possible from either of the two input datasets alone. Our study provides valuable insights on the operation and performance of an open sensor system for air quality, particularly under challenging Nordic environmental conditions. [ABSTRACT FROM AUTHOR]

In most urban areas of the world, particulate matter (PM) levels pose severe problems, addressed in several policy areas (air quality, climate change, and human health). PM presents multiple challenges due to the multitude of its sources, spanning many sectors of economic activity as well as nature, and due to the complexity of atmospheric processes involved in its transport and secondary formation. For the authorities, the goal is to assure minimal impacts of atmospheric PM levels, in practice represented by compliance with existing regulations and standards. This may be achieved through an air quality management plan (AQMP). In Northern America and in parts of Europe, comprehensive research programs have guided development of AQMP over the last forty years. This cumulated experience can be utilized by others who face the same problems, but have yet to develop their own substantial research base. The main purpose of the AQMP development process is to establish an effective and sound basis for planning and management of air quality in a selected area. This type of planning will ensure that significant sources of impacts are identified and controlled in the most cost-effective manner. The choice of tools, methods and input information is often dictated by their availability, and should be evaluated against current best practices. Important elements of the AQMP are the identification of sources and development of a complete emission inventory, the development and operation of an air quality monitoring programme, and the development and application of atmospheric dispersion models. A major task is to collect the necessary input data. The development of the AQMP will take into account: Air Quality Management System (AQMS) requirements, operational and functional structure requirements, source identification through emission inventories, source reduction alternatives, which may be implemented, mechanisms for facilitating interdepartmental cooperation in order to assure that actions are being taken and institutional building and training requirements. This paper offers a practical guide through the different parts of the air quality management and planning procedures. [ABSTRACT FROM AUTHOR]

Background/methods : Current air pollution levels pose a threat to the health of children starting from conception. The scientific evidence is presented for mortality, morbidity, and sub-clinical effects. The first section deals with exposure data, the following sections with the evidence of health effects from epidemiology and toxicology leading to recommendations. Results : Improved air quality reduces the number of infants' deaths as well as disease and pain. Conclusions: Medical doctors have a responsibility to know the facts and to advise their patients. Doctors when visiting their patients' homes should be aware of the possibly grave impact of the indoor environment for the respiratory health of their patients. They should recognize and advise the parents on problems associated with environmental tobacco smoke, poor ventilation, mould growth, and maintenance of heating installations. With regard to outdoor air pollution, doctors could serve as role models and also advise their patients and parents on environmentally friendly behaviour. Such behaviour not only calls for personal commitment but also for the right infrastructure to be provided (e.g. public transport, district heating). Doctors should be proactive in the community and in their country as advocates for a healthier environment for our children. [ABSTRACT FROM AUTHOR]

Low-cost sensors are a current trend in citizen science projects that focus on air quality. Until now, devices incorporating such sensors have been tested primarily for their technical capabilities and limitations, whereas their usability and acceptability amongst the public rarely goes beyond proof of concept, leaving user experience (UX) unstudied. The authors argue that UX should be taken into account to make sure that products and services are fit for purpose. Nineteen volunteers tested and evaluated a prototype device and provided feedback through semi-structured interviews and during focus group sessions. Their UX was then coded using mixed coding methods regarding device functionality and recommendations for future product development. The results indicate that UX can identify potentially problematic design aspects while giving deeper insights into user needs. For example, UX recognized that one of the most important aspects of user involvement and motivation was successful data harvesting, which frequently failed. This study recommends that future developers of low-cost portable air quality sensor systems prioritize reliable data transmission to minimize data loss. This will ensure an efficient and positive UX that supports user engagement in citizen science based research where collecting sensor-based data is the primary objective. [ABSTRACT FROM AUTHOR]

Within this study, attempts were made to characterize the coarse and fine particulate aerosol fractions in urban area of Belgrade and define the inorganic chemical composition of the aerosol fractions. For this purpose, daily deposits of PM10, PM2.5 and PM1 aerosol fractions were collected during spring and autumn sampling periods in 2007 and analyzed for the PM mass concentrations, trace elements and secondary ions. The results obtained in the two campaigns showed average daily mass concentrations of 37 and 44 μg/m³ for PM10, 22 and 23 μg/m³ for PM2.5 and 15 and 17 μg/m³ for the finest particulate matter fraction PM1 with the maximums exceeding the limit values set by the EU air quality regulations. A correlation with the gas-phase ambient air pollutants SO2, NO2 and O3 was found and is discussed. The concentrations of trace elements (Mg, Al, K, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Mo, Cd, Sb, Ba, Tl, Pb and Th) and secondary ions (NO3 -, SO4 2-, NH4 +, K+, Ca2+ and Na+) determined in the PM10, PM2.5 and PM1 aerosol fractions showed levels and distributions indicating soil and traffic-related sources as the main pollution sources. This study was conducted as the first step of PM assessment in order to point out main air pollution sources and suggest a remedy strategy specific for this region. [ABSTRACT FROM AUTHOR]

In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma. Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect children's health. We have installed 17 low-cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over- or under-estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo. We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce children's exposure to air pollution. [ABSTRACT FROM AUTHOR]

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R 2 of 0.89 and a root mean squared error of 14.3 μg m −3 . It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network. [ABSTRACT FROM AUTHOR]

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality. [ABSTRACT FROM AUTHOR]

The modified Target Diagram (MTD) was developed to evaluate the performance of low-cost sensors (LCS) for air quality monitoring in comparison with reference methods by reporting relative expanded uncertainty and its contributors. An MTD provides several pieces of information, including compliance with regulation, sources of error and how to diminish them, completeness and validity of LCS calibration etc. It allows the user to examine the effect of selecting different regression types and residual fitting on the LCS measurement uncertainty. The ordinary least squared regression with fitted residuals and dynamic between reference analyser uncertainty rather than constant ones yielded more realistic LCS measurement uncertainty compared to other options. The MTD is a fast visual tool to extract several pieces of information on evaluation of any candidate method against reference method. • A graphical tool to evaluate performances of sensors was developed. • Uncertainty of sensors and its' contributors can be displayed. • Checking compliance of sensors with legislation is easy and fast. • Several other information can be extracted. [ABSTRACT FROM AUTHOR]