Regional Monitoring Network Research Articles

Development of Land Use Regression (LUR) models and high-resolution spatial mapping of criteria air pollutants: Leveraging Delhi's continuous air monitoring network and remote sensing data

High-resolution air pollution maps can help researchers and governments better characterize and mitigate pollution levels in a given region. Land Use Regression (LUR) modeling is a statistical approach capable of predicting pollution levels at a high spatial resolution. In this study, we used pollution data (for the calendar year 2019) from a dense (compared to other Indian regions) regulatory monitoring network in Delhi, India, to develop simple linear and interpretable LUR models for various criteria pollutants. The observed annual mean PM2.5 and PM10 over Delhi were found to be ∼110 μg m−3 and 220 μg m−3, respectively. The PM concentration levels were 2.5–4 times higher than the prescribed national ambient standards, while the gaseous criteria pollutants were found to be within the standards (over most of the study area). The performance of the developed LUR models ranged from poor to moderate levels (adjusted-R2 values of the models were between 0.14 and 0.63). Land use and road-network related variables were found to be the most common predictors of the observed pollution levels. Moderately performing models (11 out of the developed 20) were then used to predict pollution levels at 50 m spatial intervals and to identify the most polluted districts. The advantages and limitations of using the existing regulatory network data for LUR development, and the other probable potential reasons responsible for the underperformance of the developed models are extensively discussed. To our knowledge, this is one of the few studies carried over Indian region to develop LUR models utilizing regulatory monitoring network data.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Long-term variation in exposure to NO2 concentrations in the city of Naples, Italy: Results of a citizen science project

The objective of this study is to evaluate long-term changes in the level of exposure to NO2 among the population living in the urban area of Naples (south Italy). This has been achieved by integrating data from the regional reference monitoring network with information collected during the citizen science initiative called ‘NO2, NO grazie!’ conducted in February 2020 and coordinated by the Non-Governmental Organisation ‘Cittadini per l'aria’. This citizen science campaign was based on low-cost passive samplers (Palmes tubes), providing the ability to obtain unprecedented high-resolution NO2 levels. Using a Land Use Random Forest (LURF), we extrapolated the experimental data obtained from the citizen science campaign and evaluated the changes in population exposure from 2013 to 2022 and the uncertainty associated with this assessment was quantified. The results indicate that a large proportion of the inhabitants of Naples are still exposed to high NO2 concentrations, even if strict emission containment measures are enforced. The average levels remain higher than the new interim and air quality targets suggested by the World Health Organisation. The implementation of co-created citizen science projects, where NGO and citizens actively participate alongside scientists, can significantly improve the estimation and the interpretation of official reference data.

Monitoring of the state of atmospheric air on the example of the city of Omsk

The article presents the main results of monitoring emissions of pollutants into the atmosphere in the city of Omsk in the period from 2009 to 2021. The characteristics of pollutants, their quantity, percentage of captured and disposed of are given. The work of the regional atmospheric air monitoring network has been analyzed, the territories where the air MPC is exceeded by some indicators have been identified. The results obtained allow us to conclude that the developed network of state monitoring of the city of Omsk is representative and effective.

River influence on mercury bioaccumulation in the coastal food web of Eeyou Istchee, James Bay, Canada

Spatial patterns of bioaccumulated mercury were evaluated in coastal marine food webs of east Hudson Bay and east James Bay in the boreal subarctic of Canada. Two marine species, blue mussels ( Mytilus edulis) and common eider ducks ( Somateria mollissima) that consume mussels, were collected by a regional community-based monitoring network established in five communities. Stable isotope tracers (carbon, nitrogen, sulfur, and mercury) were measured to evaluate environmental drivers of mercury spatial patterns. Mercury concentrations of blue mussels and common eiders were twofold and fivefold higher, respectively, on the James Bay coast near the community of Chisasibi compared to sites in east Hudson Bay. Liver and muscle mercury concentrations of eiders from James Bay are among the highest values reported for the circumpolar subarctic and Arctic. Multiple lines of evidence (mercury spatial patterns, crustal elements in blue mussels, and mercury isotope values of common eiders) suggest elevated mercury in the coastal food web of east James Bay may be due to mercury loading from the La Grande River, which drains one of the largest hydroelectric developments in the world. These findings highlight the importance of further research on environmental processes linking large rivers to mercury bioaccumulation in northern coastal food webs.

The Present State of the Kola Peninsula Broadband Seismic Network

This paper provides information about the main parameters of spatial broadband seismic network in the Kola region (the northeastern part of the Fennoscandian Shield). Since 2021 the seismic network has been expanded by five seismic stations and currently consists of nine stations located on the territory of the Russian Federation. Configuration of the network allows to broaden the scope of research of the Kola region lithospheric structure significantly. The prospects of integrating the newly installed stations into the automated regional seismic monitoring network are considered. The analysis of seismic noise in the places of installation of new seismic stations was carried out. It was shown that the data provided by the new broadband stations increases the accuracy of seismic events location in the research area.

Long-Term Assessment of PurpleAir Low-Cost Sensor for PM2.5 in California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment, while emerging low-cost sensors have the potential to fill in the gaps. Recent advances in air quality monitoring have produced portable, easy-to-use, low-cost, sensor-based monitors which have given a new dimension to air pollutant monitoring and have democratized the air quality monitoring process by making monitors and results directly available at the community level. This study used PurpleAir © sensors for PM2.5 assessment in California, USA. The evaluation of PM2.5 from sensors included Quality Assurance and quality control (QA/QC) procedures, assessment concerning reference-monitored PM2.5 concentrations, and the formulation of a decision support system integrating these observations using geostatistical techniques. The hourly and daily average observed PM2.5 concentrations from PurpleAir monitors followed the trends of observed PM2.5 at regulatory monitors. PurpleAir monitors also captured the peak PM2.5 concentrations due to incidents such as forest fires. In comparison with reference-monitored PM2.5 levels, it was found that PurpleAir PM2.5 concentrations were mostly higher. The most important reason for PurpleAir’s higher PM2.5 concentrations was the inclusion of moisture or water vapor as an aerosol in contrast to measurements of PM2.5 excluding water content in FEM/FRM and non-FEM/FRM monitors. Long-term assessment (2016–2023) revealed that R2 values were between 0.54 and 0.86 for selected collocated PurpleAir sensors and regulatory monitors for hourly PM2.5 concentrations. Past research studies that were conducted for mostly shorter periods resulted in higher R2 values between 0.80 and 0.98. This study aims to provide reasonable estimations of PM2.5 concentrations with high spatiotemporal resolutions based on statistical models using PurpleAir measurements. The methods of Kriging and IDW, geostatistical interpolation techniques, showed similar spatio-temporal patterns. Overall, this study revealed that low-cost, sensor-based PurpleAir sensors could be effective and reliable tools for episodic and long-term ambient air quality monitoring and developing mitigation strategies.

Refinement of the NISECI ecological index reference conditions for Italian freshwater fish communities in the eastern Emilia-Romagna region

Following the Water Framework Directive 2000/60/CE (WFD), each member state of the European Union must monitor compliance of its rivers with ecological quality standards through biological quality indicators. The New Italian Index of the Ecological State of Fish Communities (NISECI) was developed in 2017 for the assessment of fish communities, as directed by the WFD in Italian freshwater habitats. According to the WFD, the general reference conditions (GRCs) of NISECI must be refined on a regional scale through new calculation of its metrics and sub-metrics. In the present study we used environmental and ichthyological data from 457 fish samplings distributed among 299 sampling sites within 84 different water bodies collected from 1995 to 2012 to develop: 1) new lists of expected species for six homogeneous zones identified in the Reno basin (Italy) and in the eastern regional basins of the Emilia-Romagna region; and 2) the threshold values for their species-specific abundance. Results were set as refined reference conditions (RRCs) for two of the metrics used in the application of the NISECI index in the study area (i.e. X1, relating to indigenous species and X2,b, for the abundance of expected species). The RRCs were tested by applying NISECI to 24 monitoring sites of the regional surface water monitoring network (i.e., ARPAE) and comparing the results with the application of NISECI using the GRCs. Furthermore, the analytical power of the refined NISECI was evaluated by relating the findings to three expert-based blind assessments of fish community ecological status. The results confirmed an increase in refined NISECI values and its higher consistency with expert-based assessment, supporting the validity of the presented method for RRC development and its potential for application in other regions.

Impact of soil textures on agricultural drought evolution and field capacity estimation in humid regions

Soil moisture is an important indicator for monitoring agricultural drought, and is closely related to soil texture. However, the role that soil textures play in agricultural drought evolution in humid climates is not well understood. In the study, we systematically assessed the feasibility of the Soil Water Deficit Index (SWDI) for agricultural drought monitoring in humid regions, examined the impact of soil texture and groundwater depth on agricultural drought evolution, and estimated the field holding capacity for different soil textures in humid areas based on a large-scale regional soil moisture monitoring network. The results show that: (1) The SWDI has reasonably good performance in detecting severe and extreme agricultural drought, but it tends to overestimate the drought intensity during non-severe/extreme drought periods in humid regions, particularly for clay soils. (2) Sand soils are more prone to agricultural drought than clay soils, which are more resistant to severe and extreme agricultural drought with high air temperatures and low precipitation. (3) During drought aggravation, clay soil showed a delay of about 0–18 weeks from the lowest SWDI to the maximum groundwater depth, while the delay was only 0–5 weeks for non-clay soil textures. (4) The 95th percentile is proposed as the best estimator of field holding capacity for clay and loam, while Min(maxgs) is the best estimator for sand, loamy sand, and sandy loam. Overall, these findings provide insights into the role of soil texture and groundwater depth in agricultural drought evolution in humid regions, and offer practical guidance for field holding capacity estimation for different soil textures.

Shallow groundwater temperature patterns revealed through a regional monitoring well network

AbstractGroundwater temperature is a critical control on groundwater quality, geothermal system efficiency and ecosystem dynamics in receiving surface waters. Despite the known importance of groundwater temperature, there is a lack of dedicated aquifer thermal monitoring across spatial and temporal scales. Pressure transducers and other sensors installed in groundwater monitoring well networks often record temperature as a secondary function, but these comprehensive groundwater temperature data sets are seldom analysed. In this study, we analysed seasonal, interannual and spatial patterns of shallow groundwater temperatures from a regional groundwater monitoring network in Nova Scotia, Canada and compared these subsurface temperature data to air temperature data from nearby climate stations using linear regressions and Fourier analysis. The results showed that seasonal groundwater temperatures were damped (with seasonal amplitudes 3.6%–42% of air temperature amplitudes) and lagged (phase shifted 43–145 days) compared to air temperature, with notable year‐to‐year variations in both damping and lagging. Results also highlighted the role of snowpack thickness on the lowest mean monthly groundwater temperatures. Given potential impacts of climate change, land cover change, urbanization and geothermal energy development on groundwater temperatures, we encourage water authorities and regulators to begin or enhance aquifer thermal monitoring and provide guidance for capitalizing on existing monitoring well infrastructure to track temperature dynamics and changes.

A regional scale flux-based O3 risk assessment for winter wheat in northern Italy, and effects of different spatio-temporal resolutions

Tropospheric ozone (O3) is a secondary atmospheric pollutant known to cause negative effects on vegetation in terms of physiological oxidative stress, growth rate reductions and yield losses. In recent years, dose-response relationships based on the O3 stomatal flux and effects on the biomass growth have been defined for several crop species. This study was aimed at developing a dual-sink big-leaf model for winter wheat (Triticum aestivum L.) to map the seasonal Phytotoxic Ozone Dose above a threshold of 6nmolm−2s−1 (POD6) in a domain centered on the Lombardy region (Italy). The model runs on local measured data of air temperature, relative humidity, precipitation, wind speed, global radiation and background O3 concentration provided by regional monitoring networks, and includes parameterizations for the crop's geometry and phenology, the light penetration within the canopy, the stomatal conductance, the atmospheric turbulence, and the soil water availability for the plants. For the 2017 an average POD6 of 2.03mmolm−2PLA (Projected Leaf Area) was found for the Lombardy regional domain, corresponding to an average relative yield loss of 7.5%, using the finest spatio-temporal resolution (1×1km2 and 1-h). An analysis of the model's response to different spatio-temporal resolutions (from 2×2 to 50×50km2 and from 1 to 6 h) suggests that coarser resolution maps underestimated the average POD6 regional value from 8to16%, and were unable to detect O3 hotspots. Nevertheless, resolutions of 5×5km2 1-h, and 1×1km2 3-h, can still be considered reliable for the estimation of the O3 risk at the regional level since they presented relatively low root mean squared error. Furthermore, although temperature was the main limiting factor for the wheat stomatal conductance in most of the domain, soil water availability emerged as the key factor for determining the spatial patterns of the POD6.

Wildfire Smoke Monitoring for Agricultural Safety and Health in Rural Washington

ABSTRACT Objectives This study aimed to evaluate the performance of a low-cost smoke sampling platform relative to environmental and occupational exposure monitoring methods in a rural agricultural region in central Washington state. Methods We co-located the Thingy AQ sampling platform alongside cyclone-based gravimetric samplers, a nephelometer, and an environmental beta attenuation mass (E-BAM) monitor during August and September of 2020. Ambient particulate matter concentrations were collected during a smoke and non-smoke period and measurements were compared across sampling methods. Results We found reasonable agreement between observations from two particle sensors within the Thingy AQ platform and the nephelometer and E-BAM measurements throughout the study period, though the measurement range of the sensors was greater during the smoke period compared to the non-smoke period. Occupational gravimetric sampling methods did not correlate with PM2.5 data collected during smoke periods, likely due to their capture of larger particle sizes than those typically measured by PM2.5 ambient air quality instruments during wildfire events. Conclusion Data collected before and during an intense wildfire smoke episode in September 2020 indicated that the low-cost smoke sampling platform provides a strategy to increase access to real-time air quality information in rural areas where regulatory monitoring networks are sparse if sensor performance characteristics under wildfire smoke conditions are understood. Improving access to spatially resolved air quality information could help agricultural employers protect both worker and crop health as wildfire smoke exposure increases due to the impacts of climate change. Such information can also assist employers with meeting new workplace wildfire smoke health and safety rules.

Contribution to Uncertainty Propagation Associated with On-Site Calibration of Infrasound Monitoring Systems

To improve the confidence and quality of measurements produced by regional and international infrasound monitoring networks, this work investigates a methodology for propagating uncertainty associated with on-site measurement systems. We focus on the propagation of sensor calibration uncertainties. The proposed approach is applied to synthetic infrasound signals with known back azimuth and trace velocity, recorded at the array elements. Relevant input uncertainties are investigated for propagation targeting the incoming signals (noise), instrumentation (microbarometers, calibration system, wind noise reduction system), and the time-delay-of-arrival (TDOA) model (frequency band). Uncertainty propagation is performed using the Monte Carlo method to obtain the corresponding uncertainties of the relevant output quantities of interest, namely back azimuth and trace velocity. The results indicate that, at high frequencies, large sensor uncertainties are acceptable. However, at low frequencies (<0.1 Hz), even a 2∘ sensor phase uncertainty can lead to errors in the back azimuth of up to 5∘ and errors in the trace velocity of 20 m/s.

Plugging the ambient air monitoring gaps in India's national clean air programme (NCAP) airsheds

Building an effective clean air action plan for a city's air pollution problem requires an extensive network of monitoring stations to represent spatial and temporal patterns and an understanding of the sources contributing to the problem. In India, the National Clean Air Programme (NCAP) was launched in 2019 to develop clean air action plans for 131 non-attainment cities, which includes conducting source apportionment studies and establishing emission baselines. As of February 2023, only 39 cities have completed the apportionment studies. In this paper, we present the essential resources needed to strengthen the ambient air monitoring networks, for designing a representative airshed size, sampling size, and sampling frequency, to effectively track the progress made in the cities for better air quality. The NCAP cities were grouped into 104 airsheds (5.3% of the national area), collectively representing a total of 164 cities and a total population of 295 million (21% of the national total). Of these airsheds, 73 contain only one city; 18 contain two cities, and nine contain three cities. Four airsheds – Delhi, Mumbai, Indore, and Chandigarh contain 10, 8, 5, and 5 cities respectively. To measure and analyse particulate matter pollution, a total of 2118 sampling sites are recommended for the 104 airsheds. Cities could consider hybrid monitoring networks by complementing existing regulatory monitoring network with a high-density network of low-cost/sensor-grade monitors. An airshed level air quality management plan, an enhanced monitoring network, and consolidation of information on emission sources, are crucial for optimizing the clean air efforts under NCAP.

Study of Spatio-temporal Dynamics of Suspended Particles in the Atmosphere Air of Krasnoyarsk

The monthly average concentrations of suspended solids for the period 2017–2019 from the averaged raw data of the federal and regional monitoring networks were presented and compared with the established average annual standards. Maps of the spatial distribution of suspended solids concentrations in December 2019 were provided with the location of recording stations. Spatial dynamics of suspended PM1 and PM10 concentrations in urban areas were demonstrated. The necessity of further research and revealing of existing sanitary and hygienic standards violations was substantiated.

A nested machine learning approach to short-term PM2.5 prediction in metropolitan areas using PM2.5 data from different sensor networks

Many predictive models for ambient PM2.5 concentrations rely on ground observations from a single monitoring network consisting of sparsely distributed sensors. Integrating data from multiple sensor networks for short-term PM2.5 prediction remains largely unexplored. This paper presents a machine learning approach to predict ambient PM2.5 concentration levels at any unmonitored location several hours ahead using PM2.5 observations from nearby monitoring sites from two sensor networks and the location's social and environmental properties. Specifically, this approach first applies a Graph Neural Network and Long Short-Term Memory (GNN-LSTM) network to time series of daily observations from a regulatory monitoring network to make predictions of PM2.5. This network produces feature vectors to store aggregated daily observations as well as dependency characteristics to predict daily PM2.5. The daily feature vectors are then set as the precondition of the hourly level learning process. The hourly level learning again uses a GNN-LSTM network based on daily dependency information and hourly observations from a low-cost sensor network to produce spatiotemporal feature vectors capturing the combined dependency described by daily and hourly observations. Finally, the spatiotemporal feature vectors from the hourly learning process and social-environmental data are merged and used as the input to a single-layer Fully Connected (FC) network to output the predicted hourly PM2.5 concentrations. To demonstrate the benefits of this novel prediction approach, we have conducted a case study using data collected from two sensor networks in Denver, CO, during 2021. Results show that the utilization of data from two sensor networks improves the overall performance of predicting fine-level, short-term PM2.5 concentrations compared to other baseline models.

Summary of PM2.5 Measurement Artifacts Associated with the Teledyne T640 PM Mass Analyzer Under Controlled Chamber Experimental Conditions Using Polydisperse Ammonium Sulfate Aerosols and Biomass Smoke

Particulate matter (PM) is a major primary pollutant emitted during wildland fires that has the potential to pose significant health risks to individuals/communities who live and work in areas impacted by smoke events. Limiting exposure is the principle measure available to mitigate health impacts of smoke and therefore the accurate determination of ambient PM concentrations during wildland fire events is critical to protecting public health. However, monitoring air pollutants in smoke impacted environments has proven challenging in that measurement interferences or sampling conditions can result in both positive and negative artifacts. The EPA has performed research on methods for the measurement of PM2.5 in a series of laboratory based studies including evaluation in smoke. This manuscript will summarize the results of the laboratory based evaluation of federal equivalent method (FEM) analyzers for PM2.5 with particular attention being given to the Teledyne-API Model T640 PM Mass analyzer, as compared to the filter-based federal reference method (FRM). The T640 is an optical-based PM analyzer and has been gaining wide use by state and local agencies in monitoring for PM2.5 U.S. National Ambient Air Quality Standards (NAAQS) attainment. At present, the T640 (includes both T640 and T640X) comprises ~40% of the PM2.5 FEM monitors in U.S. regulatory monitoring networks. In addition, the T640 has increasingly been employed for the higher time resolution comparison/evaluation of low-cost PM sensors including during smoke impacted events. Results from controlled non-smoke laboratory studies using generated ammonium sulfate aerosols, demonstrated a generally negative T640 measurement artifact that was significantly related to the PM2.5 concentration and particle size distribution. Results from biomass burning chamber studies demonstrated positive and negative artifacts significantly associated with PM2.5 concentration and optical wavelength dependent absorption properties of the smoke aerosol. Implications The results detailed in this product will provide state and local air monitoring agencies with the tools and knowledge to address PM2.5 measurement challenges in areas frequently impact by wildland fire smoke. The observed large positive and negative artifacts in the T640 PM mass determination has the potential to result in false exceedances of the PM2.5 NAAQS or in the disqualification of monitoring data through an exceptional event designation. In addition, the observed artifacts in smoke impacted air will have a detrimental effect on providing reliable public information when wildfires occur and also in identifying reference measurements for small sensor evaluation studies. Other PM2.5 FEMs such as the BAM-1022 perform better in smoke and are comparable to the filter based FRM. Care must be taken in choosing high time resolution FEM monitors that will be operated at smoke impacted sites. Accurate methods, such as the FRM and BAM-1022 will reduce the burden of developing and reviewing exceptional event request packages, data loss/disqualification, and provide states with tools to adequately evaluate public exposure risks and provide accurate public health messaging during wildfire/smoke events.

Wildfires Impact Assessment on PM Levels Using Generalized Additive Mixed Models

Wildfires are relevant sources of PM emissions and can have an important impact on air pollution and human health. In this study, we examine the impact of wildfire PM emissions on the Piemonte (Italy) air quality regional monitoring network using a Generalized Additive Mixed Model. The model is implemented with daily PM10 and PM2.5 concentrations sampled for 8 consecutive years at each monitoring site as the response variable. Meteorological data retrieved from the ERA5 dataset and the observed burned area data stored in the Carabinieri Forest Service national database are used in the model as explanatory variables. Spline functions for predictive variables and smooths for multiple meteorological variables’ interactions improved the model performance and reduced uncertainty levels. The model estimates are in good agreement with the observed PM data: adjusted R2 range was 0.63–0.80. GAMMs showed rather satisfactory results in order to capture the wildfires contribution: some severe PM pollution episodes in the study area due to wildfire air emissions caused peak daily levels up to 87.3 µg/m3 at the Vercelli PM10 site (IT1533A) and up to 67.7 µg/m3 at the Settimo Torinese PM2.5 site (IT1130A).

Low-Cost Sensor Node for Air Quality Monitoring: Field Tests and Validation of Particulate Matter Measurements.

Air pollution is still a major public health issue, which makes monitoring air quality a necessity. Mobile, low-cost air quality measurement devices can potentially deliver more coherent data for a region or municipality than stationary measurement stations are capable of due to their improved spatial coverage. In this study, air quality measurements obtained during field tests of our low-cost air quality sensor node (sensor-box) are presented and compared to measurements from the regional air quality monitoring network. The sensor-box can acquire geo-tagged measurements of several important pollutants, as well as other environmental quantities such as light and sound. The field test consists of sensor-boxes mounted on utility vehicles operated by municipalities located in Central Switzerland. Validation is performed against a measurement station that is part of the air quality monitoring network of Central Switzerland. Often not discussed in similar studies, this study tests and discusses several data filtering methods for the removal of outliers and unfeasible values prior to further analysis. The results show a coherent measurement pattern during the field tests and good agreement to the reference station during the side-by-side validation test.