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

A parent-school initiative to assess and predict air quality around a heavily trafficked school

Many primary schools in the UK are situated in close proximity to heavily-trafficked roads, yet long-term air pollution monitoring around such schools to establish factors affecting the variability of exposure is limited. We co-designed a study to monitor particulate matter in different size fractions (PM₁, PM_2.5, PM₁₀), gaseous pollutants (NO₂, O₃ and CO) and meteorological parameters (ambient temperature, relative humidity) over a period of one year. The period included phases of national COVID-19 lockdown and its subsequent easing and removal. Statistical analysis was used to assess the diurnal patterns, pollution hotspots and underlying factors driving changes. A pollution episode was observed early in January 2021, owing to new year celebration fireworks, when the daily average PM_2.5 was around three-times the World Health Organisation limit. PM_2.5 and NO₂ exceeded the threshold limits on 15 and 10 days, respectively, as the lockdown eased and the school reopened, despite the predominant wind direction often being away from the school towards the roads. The peak concentration levels for all pollutants occurred during morning drop-off hours; however, some weekends showed higher or comparable concentrations to those during weekdays. The strong disproportional Pearson correlation between CO and temperature demonstrated the possible contribution of local sources through biomass burning. The impact of lifting restrictions after removing the weather impact showed that the average pollution levels were low in the beginning and increased by up to 22.7 % and 4.2 % for PM_2.5 and NO₂, respectively, with complete easing of lockdown. The Prophet algorithm was implemented to develop a prediction model using an NO₂ dataset that performed moderately (R², 0.48) for a new monthly dataset. This study was able to build a local air pollution database at a school gate, which enabled an understanding of the air pollution variability across the year and allowed evidence-based mitigation strategies to be devised.

Spatial Equity of PM2.5 Pollution Exposures in High-Density Metropolitan Areas Based on Remote Sensing, LBS and GIS Data: A Case Study in Wuhan, China

In-depth studies have been conducted on the risk of exposure to air pollution in urban residents, but most of them are static studies based on the population of residential units. Ignoring the real environmental dynamics during daily activity and mobility of individual residents makes it difficult to accurately estimate the level of air pollution exposure among residents and determine populations at higher risk of exposure. This paper uses the example of the Wuhan metropolitan area, high-precision air pollution, and population spatio-temporal dynamic distribution data, and applies geographically weighted regression models, bivariate LISA analysis, and Gini coefficients. The risk of air pollution exposure in elderly, low-age, and working-age communities in Wuhan was measured and the health equity within vulnerable groups such as the elderly and children was studied. We found that ignoring the spatio-temporal behavioral activities of residents underestimated the actual exposure hazard of PM_2.5 to residents. The risk of air pollution exposure was higher for the elderly than for other age groups. Within the aging group, a few elderly people had a higher risk of pollution exposure. The high exposure risk communities of the elderly were mainly located in the central and sub-center areas of the city, with a continuous distribution characteristic. No significant difference was found in the exposure risk of children compared to the other populations, but a few children were particularly exposed to pollution. Children's high-exposure communities were mainly located in suburban areas, with a discrete distribution. Compared with the traditional static PM_2.5 exposure assessment, the dynamic assessment method proposed in this paper considers the high mobility of the urban population and air pollution. Thus, it can accurately reveal the actual risk of air pollution and identify areas and populations at high risk of air pollution, which in turn provides a scientific basis for proposing planning policies to reduce urban PM_2.5 and improve urban spatial equity.

Applications of artificial intelligence in the field of air pollution: A bibliometric analysis

Background

Artificial intelligence (AI) has become widely used in a variety of fields, including disease prediction, environmental monitoring, and pollutant prediction. In recent years, there has also been an increase in the volume of research into the application of AI to air pollution. This study aims to explore the latest trends in the application of AI in the field of air pollution.

Methods

All literature on the application of AI to air pollution was searched from the Web of Science database. CiteSpace 5.8.R1 was used to analyze countries/regions, institutions, authors, keywords and references cited, and to reveal hot spots and frontiers of AI in atmospheric pollution.

Results

Beginning in 1994, publications on AI in air pollution have increased in number, with a surge in research since 2017. The leading country and institution were China (N = 524) and the Chinese Academy of Sciences (N = 58), followed by the United States (N = 455) and Tsinghua University (N = 33), respectively. In addition, the United States (0.24) and the England (0.27) showed a high degree of centrality. Most of the identified articles were published in journals related to environmental science; the most cited journal was Atmospheric Environment, which reached nearly 1,000 citations. There were few collaborations among authors, institutions and countries. The hot topics were machine learning, air pollution and deep learning. The majority of the researchers concentrated on air pollutant concentration prediction, particularly the combined use of AI and environmental science methods, low-cost air quality sensors, indoor air quality, and thermal comfort.

Conclusion

Researches in the field of AI and air pollution are expanding rapidly in recent years. The majority of scholars are from China and the United States, and the Chinese Academy of Sciences is the dominant research institution. The United States and the England contribute greatly to the development of the cooperation network. Cooperation among research institutions appears to be suboptimal, and strengthening cooperation could greatly benefit this field of research. The prediction of air pollutant concentrations, particularly PM_2.5, low-cost air quality sensors, and thermal comfort are the current research hotspot.

Low-cost monitoring of atmospheric PM-development and testing

Ambient particulate matter (PM) pollution is a significant problem in many urban and rural regions and has severe human health implications. Real-time, spatially dense monitoring using a network of low-cost sensors (LCS) was previously proposed as a way to alleviate the problem of PM. In this study, the performance of an LCS (Plantower PMS7003), a candidate for use in such a network, was investigated. The sensor was calibrated in a controlled climate chamber against a standard reference aerosol monitor. Reproducibility and calibration were evaluated in laboratory tests. Long-term, in-field performance was studied via deploying an LCS assembly at an environmental monitoring station. Results indicated excellent unit-to-unit consistency; however, each sensor needed to be calibrated individually as their characteristics varied slightly. Based on the results of a 15-month field test, quantitative and indicative LCS performance appeared promising: overall indicative accuracy was approximately 73-75% with comparable precision and recall. It is advised that the LCS are cleaned after 6-8 months of operation. Overall, the LCS appeared suitable for low-cost monitoring.

Semiquantitative Classification of Two Oxidizing Gases with Graphene-Based Gas Sensors

Miniature and low-power gas sensing elements are urgently needed for a portable electronic nose, especially for outdoor pollution monitoring. Hereby we prepared chemiresistive sensors based on wide-area graphene (grown by chemical vapor deposition) placed on Si/Si3N4 substrates with interdigitated electrodes and built-in microheaters. Graphene of each sensor was individually functionalized with ultrathin oxide coating (CuO-MnO2, In2O3 or Sc2O3) by pulsed laser deposition. Over the course of 72 h, the heated sensors were exposed to randomly generated concentration cycles of 30 ppb NO2, 30 ppb O3, 60 ppb NO2, 60 ppb O3 and 30 ppb NO2 + 30 ppb O3 in synthetic air (21% O2, 50% relative humidity). While O3 completely dominated the response of sensors with CuO-MnO2 coating, the other sensors had comparable sensitivity to NO2 as well. Various response features (amplitude, response rate, and recovery rate) were considered as machine learning inputs. Using just the response amplitudes of two complementary sensors allowed us to distinguish these five gas environments with an accuracy of ~ 85%. Misclassification was mostly due to an overlap in the case of the 30 ppb O3, and 30 ppb O3 + 30 ppb NO2 responses, and was largely caused by the temporal drift of these responses. The addition of recovery rates to machine learning input variables enabled us to very clearly distinguish different gases and increase the overall accuracy to ~94%.

Noise and air pollution during Covid-19 lockdown easing around a school site

During the Covid-19 pandemic and resulting lockdowns, road traffic volumes reduced significantly leading to reduced pollutant concentrations and noise levels. Noise and the air pollution data during the lockdown period and loosening of restrictions through five phases in 2021 are examined for a school site in the United Kingdom. Hourly and daily average noise level as well as the average over each phase, correlations between noise and air pollutants, variations between pollutants, and underlying reasons explaining the temporal variations are explored. Some strong linear correlations were identified between a number of traffic-sourced air pollutants, especially between the differently sized particulates PM₁, PM_2.5, and PM₁₀ (0.70 < r <0.98) in all phases and an expected inverse correlation between nitrogen dioxide (NO₂) and ground-level ozone (O₃) (-0.68 < r < -0.78) as NO₂ is a precursor of O₃. Noise levels exhibit a weak correlation with the measured air pollutants and moderate correlation with meteorological factors, including wind direction, temperature, and relative humidity. There was a consistent and significant increase in noise levels (p < 0.01) of up to 3 dB with initial easing, and this was maintained through the remaining phases.

Personal Exposure to Black Carbon, Particulate Matter and Nitrogen Dioxide in the Paris Region Measured by Portable Sensors Worn by Volunteers

Portable sensors have emerged as a promising solution for personal exposure (PE) measurement. For the first time in Île-de-France, PE to black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO₂) was quantified based on three field campaigns involving 37 volunteers from the general public wearing the sensors all day long for a week. This successful deployment demonstrated its ability to quantify PE on a large scale, in various environments (from dense urban to suburban, indoor and outdoor) and in all seasons. The impact of the visited environments was investigated. The proximity to road traffic (for BC and NO₂), as well as cooking activities and tobacco smoke (for PM), made significant contributions to total exposure (up to 34%, 26%, and 44%, respectively), even though the time spent in these environments was short. Finally, even if ambient outdoor levels played a role in PE, the prominent impact of the different environments suggests that traditional ambient monitoring stations is not a proper surrogate for PE quantification.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Traffic exposure, air pollution and children's physical activity at early childhood education and care

BACKGROUND

A significant number of children attend Early Childhood Education and Care (ECEC). ECEC is an important environment and behaviour setting for young children. Time spent outdoors is positively associated with children's physical activity levels, yet increased time spent physically active outdoors may expose young children to traffic-related air pollution, particularly in ECEC centres located in high traffic areas.

METHODS

This study was part of the Play Spaces and Environments for Children's Physical Activity (PLAYCE) study, Perth, Western Australia. Data from 22 ECEC centres and 478 children were collected. Continuous measures of indoor and outdoor fine particulate matter (PM_2.5) were conducted for 48-72 h in each ECEC. Children wore ActiGraph GT3X + accelerometers to measure their physical activity at ECEC. The total length of high traffic roads within a 300m road network service area buffer around each ECEC was used to identify high and low traffic centres.

RESULTS

Outdoor PM_2.5 concentrations peaked in the afternoon (1pm, 2pm and 6pm) at ECEC centres. Outdoor and indoor PM_2.5 concentrations were significantly higher for centres located in high compared with low traffic areas (both p < 0.05). There was no significant association between a centre being located in a high or low traffic area and the time preschoolers spent outdoors or their physical activity levels.

DISCUSSION

Time periods when air pollution concentrations in ECECs are highest correspond with times when preschoolers are likely to be physically active outdoors. Children's potential exposure to traffic-related air pollutants is occurring during a period of rapid lung development. Given there is no evidence of a safe level of exposure to PM_2.5 or a threshold below which no adverse health effects occur, careful planning should be a consideration to avoid locating ECEC centres in high traffic areas.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

Assessment of PM2.5 Exposure during Cycle Trips in The Netherlands Using Low-Cost Sensors

Air pollution, especially fine particulate matter (PM2.5), is a major environmental risk factor for human health in Europe. Monitoring of air quality takes place using expensive reference stations. Low-cost sensors are a promising addition to this official monitoring network as they add spatial and temporal resolution at low cost. Moreover, low-cost sensors might allow for better characterization of personal exposure to PM2.5. In this study, we use 500 dust (PM2.5) sensors mounted on bicycles to estimate typical PM2.5 levels to which cyclists are exposed in the province of Utrecht, the Netherlands, in the year 2020. We use co-located sensors at reference stations to calibrate and validate the mobile sensor data. We estimate that the average exposure to traffic related PM2.5, on top of background concentrations, is approximately 2 μg/m3. Our results suggest that cyclists close to major roads have a small, but consistently higher exposure to PM2.5 compared to routes with less traffic. The results allow for a detailed spatial representation of PM2.5 concentrations and show that choosing a different cycle route might lead to a lower exposure to PM2.5. Finally, we conclude that the use of mobile, low-cost sensors is a promising method to estimate exposure to air pollution.

From a Low-Cost Air Quality Sensor Network to Decision Support Services: Steps towards Data Calibration and Service Development

Air pollution is a widespread problem due to its impact on both humans and the environment. Providing decision makers with artificial intelligence based solutions requires to monitor the ambient air quality accurately and in a timely manner, as AI models highly depend on the underlying data used to justify the predictions. Unfortunately, in urban contexts, the hyper-locality of air quality, varying from street to street, makes it difficult to monitor using high-end sensors, as the cost of the amount of sensors needed for such local measurements is too high. In addition, development of pollution dispersion models is challenging. The deployment of a low-cost sensor network allows a more dense cover of a region but at the cost of noisier sensing. This paper describes the development and deployment of a low-cost sensor network, discussing its challenges and applications, and is highly motivated by talks with the local municipality and the exploration of new technologies to improve air quality related services. However, before using data from these sources, calibration procedures are needed to ensure that the quality of the data is at a good level. We describe our steps towards developing calibration models and how they benefit the applications identified as important in the talks with the municipality.

Evaluation of field calibration methods and performance of AQMesh, a low-cost air quality monitor

Field calibrations of NO₂, NO, and PM₁₀ from AQMesh Air Quality Monitors (AQMs) were conducted during a summer and an autumn period in a busy street in a midsize Swedish city. All the three linear calibration procedures studied (postscaled, bisquare, and orthogonal data) significantly reduced the ranges and magnitudes of the performance indicators to yield more reliable results than the raw data. The improvements were sufficient to satisfy the European Union (EU) Data Quality Objective (DQO) for indicative measurements as compared to reference data only for NO₂ (above 50 µg m^-3) and NO (above 30 µg m^-3) during the autumn calibration period. The relatively simple bisquare procedure had the best performance overall. The bisquare procedure improved the root mean square error by the same amount as other studies using complex multivariate calibration methods. Low concentrations of pollutants were measured, far below the EU Environmental Quality Standard thresholds and even satisfying the future goals for the Environmental Quality Objectives. Cleaning the raw data by removing data points in the reference data that were below the reference station limit of detections (and the synchronous data points in the AQM prescaled data) was found to improve the performances of the calibration procedures appreciably. Many NO₂ and almost all PM₁₀ data points in this study fell below the AQM limit of detection. These low concentrations will probably be a common problem in many field studies, at least in areas with relatively low air pollution. However, the relative errors were sufficiently low for these data points that they could be interpreted as accurately representing low concentrations and did not need to be removed from the datasets. For the NO₂ measurements, a slight periodic error correlated with sunlight and increased ambient temperature was noted. NO measurements correlated strongly with increased traffic.

Outlier detection and gap filling methodologies for low-cost air quality measurements

Air pollution is a major environmental health problem around the world, which needs to be monitored. In recent years, a new generation of low-cost air pollution sensors has emerged. Poor or unknown data quality, resulting from the intrinsic properties of the sensor as well as the lack of a consensus on data processing methodologies for these sensors, has, among other factors, prevented widespread adoption of these sensors. To contribute to the creation of this consensus, we reviewed the available methodologies for quality control, outlier detection and gap filling and applied two outlier detection methodologies and five gap filling methodologies to a case study (consisting of an 11-month long air quality data set from a low-cost sensor). We showed that erroneous data can be detected in a fully automated way, and that point and contextual outlier detection methodologies can be applied to low-cost air pollution data and yield meaningful results. The linear interpolation showed the best performance for gap filling for low-cost air pollution sensors. In conclusion, data cleaning procedures are important, and the presented methods can form part of a generalised data processing methodology for low-cost air pollution sensors.

Assessing the impact of air pollution on childhood asthma morbidity: how, when, and what to do

PURPOSE OF REVIEW

Exposure to air pollutants is linked with poor asthma control in children and represents a potentially modifiable risk factor for impaired lung function, rescue medication use, and increased asthma-related healthcare utilization. Identification of the most relevant pollutants to asthma as well as susceptibility factors and strategies to reduce exposure are needed to improve child health.

RECENT FINDINGS

The current available literature supports the association between pollutants and negative asthma outcomes. Ethnicity, socioeconomic status, and presence of certain gene polymorphisms may impact susceptibility to the negative health effects of air pollution. Improved air quality standards were associated with better asthma outcomes.

SUMMARY

The link between air pollution and pediatric asthma morbidity is supported by the recent relevant literature. Continued efforts are needed to identify the most vulnerable populations and develop strategies to reduce exposures and improve air quality.

Performance Assessment of a Low-Cost PM2.5 Sensor for a near Four-Month Period in Oslo, Norway

The very low-cost Nova particulate matter (PM) sensor SDS011 has recently drawn attention for its use for measuring PM mass concentration, which is frequently used as an indicator of air quality. However, this sensor has not been thoroughly evaluated in real-world conditions and its data quality is not well documented. In this study, three SDS011 sensors were evaluated by co-locating them at an official, air quality monitoring station equipped with reference-equivalent instrumentation in Oslo, Norway. The sensors’ measurement results for PM2.5 were compared with data generated from the air quality monitoring station over almost a four-month period. Five performance aspects of the sensors were examined: operational data coverage, linearity of response and accuracy, inter-sensor variability, dependence on relative humidity (RH) and temperature (T), and potential improvement of sensor accuracy, by data calibration using a machine-learning method. The results of the study are: (i) the three sensors provide quite similar results, with inter-sensor correlations exhibiting R values higher than 0.97; (ii) all three sensors demonstrate quite high linearity against officially measured concentrations of PM2.5, with R2 values ranging from 0.55 to 0.71; (iii) high RH (over 80%) negatively affected the sensor response; (iv) data calibration using only the RH and T recorded directly at the three sensors increased the R2 value from 0.71 to 0.80, 068 to 0.79, and 0.55 to 0.76. The results demonstrate the general feasibility of using these low cost SDS011 sensors for indicative PM2.5 monitoring under certain environmental conditions. Within these constraints, they further indicate that there is potential for deploying large networks of such devices, due to the sensors’ relative accuracy, size and cost. This opens up a wide variety of applications, such as high-resolution air quality mapping and personalized air quality information services. However, it should be noted that the sensors exhibit often very high relative errors for hourly values and that there is a high potential of abusing these types of sensors if they are applied outside the manufacturer-provided specifications particularly regarding relative humidity. Furthermore, our analysis covers only a relatively short time period and it is desirable to carry out longer-term studies covering a wider range of meteorological conditions.

An air quality index-based multistage type-2-fuzzy interval-stochastic programming model for energy and environmental systems management under multiple uncertainties

In this study, a multistage type-2-fuzzy interval-stochastic programming (MTIP) method is developed, which extends upon the existing multistage stochastic programming (MSP) by allowing uncertainties expressed as probabilistic distributions, interval values and type-2 fuzzy sets to be effectively incorporated within the optimization framework. Through coupling air quality index (AQI) with MTIP, an AQI-MTIP model is formulated for energy and environmental systems (EES) management of Tianjin. A number of scenarios based on changed AQIs are examined to analyze the impacts of environmental requirements on the city's energy system. Results indicate that (i) with the improvement of environmental requirement, utilization of clean energies (especially natural gas) is provoked markedly; (ii) PM_2.5 is the primary pollutant, 64.50% of which should be reduced each period to maintain the city's air quality at a health-safe level. These findings can help decision makers adjust energy structure, make effective mitigation strategy, and gain deep insight into the relationship between energy consumption and environmental requirement.