Nuria Castell

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

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.

Modelling atmospheric oxidation of 2-aminoethanol (MEA) emitted from post-combustion capture using WRF-Chem

Carbon capture and storage (CCS) is a technological solution that can reduce the amount of carbon dioxide (CO2) emissions from the use of fossil fuel in power plants and other industries. A leading method today is amine based post-combustion capture, in which 2-aminoethanol (MEA) is one of the most studied absorption solvents. In this process, amines are released to the atmosphere through evaporation and entrainment from the CO2 absorber column. Modelling is a key instrument for simulating the atmospheric dispersion and chemical transformation of MEA, and for projections of ground-level air concentrations and deposition rates. In this study, the Weather Research and Forecasting model inline coupled with chemistry, WRF-Chem, was applied to quantify the impact of using a comprehensive MEA photo-oxidation sequence compared to using a simplified MEA scheme. Main discrepancies were found for iminoethanol (roughly doubled in the detailed scheme) and 2-nitro aminoethanol, short MEA-nitramine (reduced by factor of two in the detailed scheme). The study indicates that MEA emissions from a full-scale capture plant can modify regional background levels of isocyanic acid. Predicted atmospheric concentrations of isocyanic acid were however below the limit value of 1 ppbv for ambient exposure. The dependence of the formation of hazardous compounds in the OH-initiated oxidation of MEA on ambient level of nitrogen oxides (NOx) was studied in a scenario without NOx emissions from a refinery area in the vicinity of the capture plant. Hourly MEA-nitramine peak concentrations higher than 40 pg m(-3) did only occur when NOx mixing ratios were above 2 ppbv. Therefore, the spatial variability and temporal variability of levels of OH and NOx need to be taken into account in the health risk assessment. The health risk due to direct emissions of nitrosamines and nitramines from full-scale CO2 capture should be investigated in future studies.

Modeling PM10 Originating from Dust Intrusions in the Southern Iberian Peninsula Using HYSPLIT

The Hybrid Single-Particle Lagrangian Integrated Trajectories (HYSPLIT) model has been applied to calculate the spatial and temporal distributions of dust originating from North Africa. The model has been configured to forecast hourly particulate matter #10 mm (PM10) dust concentrations focusing on the impacts over the southern Iberian Peninsula. Two full years (2008 and 2009) have been simulated and compared against surface background measurement sites. A statistical analysis using discrete and categorical evaluations is presented. The model is capable of simulating the occurrence of Saharan dust episodes as observed at the measurement stations and captures the generally higher levels observed in eastern Andalusia, Spain, with respect to the western Andalusia station. But the simulation tends to underpredict the magnitude of the dust concentration peaks. The model has also been qualitatively compared with satellite data, showing generally good agreement in the spatial distribution of the dust column.

Modeling and evaluation of urban pollution events of atmospheric heavy metals from a large Cu-smelter.

Metal smelting and processing are highly polluting activities that have a strong influence on the levels of heavy metals in air, soil, and crops. We employ an atmospheric transport and dispersion model to predict the pollution levels originated from the second largest Cu-smelter in Europe. The model predicts that the concentrations of copper (Cu), zinc (Zn), and arsenic (As) in an urban area close to the Cu-smelter can reach 170, 70, and 30 ng m−3, respectively. The model captures all the observed urban pollution events, but the magnitude of the elemental concentrations is predicted to be lower than that of the observed values; ~300, ~500, and ~100 ng m−3 for Cu, Zn, and As, respectively. The comparison between model and observations showed an average correlation coefficient of 0.62 ± 0.13. The simulation shows that the transport of heavy metals reaches a peak in the afternoon over the urban area. The under-prediction in the peak is explained by the simulated stronger winds compared with monitoring data. The stronger simulated winds enhance the transport and dispersion of heavy metals to the regional area, diminishing the impact of pollution events in the urban area. This model, driven by high resolution meteorology (2 km in horizontal), predicts the hourly-interval evolutions of atmospheric heavy metal pollutions in the close by urban area of industrial hotspot.

Mapping urban air quality in near real-time using observations from low-cost sensors and model information

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 R2 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.

Localized real-time information on outdoor air quality at kindergartens in Oslo, Norway using low-cost sensor nodes

Abstract 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 childrens 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 childrens exposure to air pollution. HighlightsWe show that low‐cost sensors can provide an indication of outdoor air quality.We employed a data fusion method to provide real‐time air quality maps.Localized real‐time air quality information can help parents and kindergarten staff.

Supporting Sustainable Mobility Using Mobile Technologies and Personalized Environmental Information: The Citi-Sense-MOB Approach in Oslo, Norway

Urban and peri-urban growth is increasing worldwide and Europe is now one of the most urbanized continents in the world. Oslo is one of the fastest growing cities in Europe. This creates pressure on its infrastructure, including traffic and environmental urban quality. Additionally, vehicular traffic is a major contributor to CO2 emissions, which affects climate change. It is recognized that air quality is a major factor for human health, however, although different measures have been implemented, improving air quality and lowering carbon emissions still remains an unsolved problem in Oslo.

A Network of Low-Cost Air Quality Sensors and Its Use for Mapping Urban Air Quality

Recent rapid technological advances in sensor technology have resulted in a wide variety of small and low-cost microsensors with significant potential for measuring air pollutants. In this contribution, we evaluate the performance of a commercially available low-cost sensor platform for air quality and show how the data from a network of such devices can be used for high-resolution mapping of urban air quality. Our results indicate that the sensor platforms are subject to a significant sensor-to-sensor variability as well as strong dependencies on environmental conditions. A field calibration of all individual sensor devices by co-locating them with an air quality monitoring station equipped with reference instrumentation is thus required for obtaining the best possible results. We further demonstrate that, despite relatively low accuracy at the individual sensor level, a methodology based on geostatistical data fusion is capable of merging the information from the sensor network with model information in such a way that we can obtain realistic and frequently updated maps of urban air quality. We show that exploiting the “swarm knowledge” of the entire network of sensors is capable of extracting useful information from the data even though individual sensors are subject to significant uncertainty.

Assessing the Relocation Robustness of on Field Calibrations for Air Quality Monitoring Devices

The adoption of on field calibration for pervasive air quality monitors, is increasing significantly in the last few years. The sensors data, recorded on the field, together with co-located reference analyzers data, allow to build a knowledge base that is more representative of the real world conditions and thus more effective. However, on field calibration precision may fade in time due to change in operative conditions, due to different drivers. Among these, relocation is deemed among the most relevant. In this work, for the first time, we attempt to assess the robustness of this approach to relocation of the sensor nodes. We try to evaluate the impact on performance of the so called locality issue by measuring the changes in the performance indicators, when a chemical multisensory system operates in a location that differs from the one in which it was on field calibrated. To this purposes, a nonlinear multivariate approach with Neural Networks (NN) and a suitable dataset, provided by NILU (the Norwegian Institute for Air Quality), have been used. The preliminary results show a greater influence of seasonal forcers distribution with respect to the relocation issues.

Is on field calibration strategy robust to relocation

On Field calibration is increasingly considered as the best performing approach for air quality monitor devices. Field recorded sensor data together with co-located reference data build suitable dataset that are more representative of the complexity of real world conditions. However, many researchers pointed out the possible lack of generalization due to the strong dependence on the condition encountered during the field recordings. This work, for the first time, try to assess the robustness of this approach to relocation of the sensor nodes. This is particular relevant for mobile deployments and for guaranteeing the scalability properties of this calibration approach in pervasive deployments. Neural Networks have been used to provide for a nonlinear multivariate calibration algorithm. An extensive dataset, recorded in Oslo during 2015–16, provided the ground for a multi-node/multi-weeks assessment. The observed differences account for a greater influence of seasonal changes on the performances with respect to relocation effects.