Reference Air Quality Research Articles

Low‐cost air quality monitoring networks for long‐term field campaigns: A review

AbstractThe application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges.

School exposure to air pollution in the UK and Ireland: Indoor-Outdoor air quality assessment

Abstract Background Globally, 93% of children under the age of 15 (1.8 billion) are exposed to fine particulate matter (PM2.5) levels that might cause respiratory/cardiovascular disorders, impaired neuro/cognitive development, and cancer. Air pollution causes 1/10 deaths in children under the age of 5. Methods The purpose of this observational study was to collect data on indoor and outdoor air quality in 24 schools over 10 school days and compare it with city-wide average air quality levels reported in rural/urban areas in the UK and Northern Ireland. Nitrogen dioxide, volatile organic compounds (VOCs) and PM10,2.5,1 were monitored inside the classrooms and school surroundings with personal pollution monitors. City-wide averages were taken from local reference air quality and ground-source monitoring stations. Data was expressed using the Plume Labs Air Quality Index (PAQI) thresholds [low (0-20), moderate (21-50), high (51-100), very high (101+) pollution level] aligned with WHO's exposure recommendations. Results Despite both AQIs being in the moderate threshold, the school's average indoor AQI was ∼44% higher than the city-wide average. Only 27% of schools’ indoor air quality indexes matched the city-wide average, 59% were higher and 14% were lower. Rural schools had a marginally higher average indoor AQI than urban schools. The primary classroom pollutants observed were VOCs, followed by PM10. The average walk-exposure AQI of 33% of total schools was higher than the city's average. The school reporting the highest average walk AQI was seven times higher than the lowest. Conclusions Poor indoor air quality and higher levels of pollutants were found inside the majority of the schools across the regions. There is a need for mitigation strategies to identify responsible factors for indoor air pollution. A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce pollution exposure and its impact on children's health outcomes and mortality. Key messages • Poor indoor air quality (∼44% higher than the city-wide average) and higher levels of pollutants (volatile organic compounds and PM10) were found inside the majority of the schools across the regions. • A regulatory framework for school ventilation and behavior interventions like nasal washes may reduce air pollution exposure and its impact on children’s health outcomes and mortality.

Short-Term Field Evaluation of Low-Cost Sensors Operated by the “AirSensEUR” Platform

Electrochemical low-cost sensors, suitable for the monitoring of different air quality parameters such as carbon monoxide or nitrogen dioxide levels, are viable tools for creating affordable handheld devices for short-term or dense air quality monitoring networks for long-term measurements and IoT applications. However, most devices that utilize such sensors are based on proprietary hardware and software and, therefore, do not offer users the ability to replace sensors or interact with the hardware, software, and data in a meaningful way. Initiatives that focus on an open framework for air quality monitoring, such as the AirSensEUR project, offer competitive open source alternatives. In this study, we examined the feasibility of the application of such devices. Five AirSensEUR units equipped with chemical sensors were placed next to a reference air quality measuring station in Vienna, Austria. During co-location, concentrations of 0.20 ± 0.06 ppm, 7.14 ± 8.66 ppb, and 17.58 ± 9.90 ppb were measured for CO, NO, and NO2, respectively. The process of evaluating the performance of the low-cost sensors was carried out and compared to similar studies. Data analysis was carried out with the help of the basic functions in MS Excel. We investigated the linear correlation between the sensor and reference data and thus calculated the coefficient of determination, the average and maximum residuals, and the correlation coefficient. Furthermore, we discuss sensor properties in regard to selectivity and long-term stability.

A new approach for roughness representation within urban dispersion models

The effects of green infrastructure on pollutant concentrations are greatly variable, essentially depending on the surrounding built-up environment and on local meteorological conditions. To simulate the effects of the presence of trees at urban scale, a reliable methodology is the Computational Fluid Dynamics (CFD) approach, however it needs high calculation costs. An alternative integral dispersion model is given by provided that a suitable parameterization for vegetation is included. In this work, we have developed and demonstrated a novel methodology, based on aerodynamic parameters, to include the aerodynamic effect of trees in an operational dispersion model, the ADMS-Urban. The aerodynamic parameters were derived using the morphometric method starting from open data containing information on buildings and trees. The new roughness parameter calculation method has produced the urban spatially varying roughness (USVR) and it was evaluated in different scenarios at the urban and neighborhood scale. The numerical outputs of the simulations were compared with observations from reference air quality stations collected within an ad-hoc intensive field campaign conducted in 2017 in the city of Bologna, Italy. The results of the comparison highlight that the introduction of the aerodynamic effects of buildings lead to great improvements in the performance of the model at both spatial scales and for the different study sites considered in this study. Conversely, the inclusion of trees in the calculation produces significant improvements only when conducting studies at high spatial resolution and for densely vegetated areas.

True Reduction in the Air Pollution Levels in the Community of Madrid During the COVID-19 Lockdown

The coronavirus disease (COVID) lockdown was implemented in 2020, which included harsh restrictions on the amount of traffic. As a consequence, a low-emission scenario that could only be simulated before, actually occurred. This constituted a unique and valuable opportunity to study the effect of air quality pollutant concentrations. Although a direct comparison between the observed measured values given by reference air quality stations (AQSs) and values from before the COVID lockdown provides an idea of the pollution reduction, it cannot be separated from the meteorology, and hence, those studies could be misleading. This study used the approach of modeling a normal business day using both air quality and meteorological data from 2017 to 2019 to train machine-learning models to be able to predict what concentration of the three most concerning pollutants (NO2, O3, and PM10) would be given by the meteorological conditions and the time of the year. The XGBoost and LightGBM gradient boosting decision tree-based models were applied to the time series recorded in Madrid and used to predict the expected concentrations in 2020 if no restrictions had been made. The predictions could then be compared to the real observed AQS data to determine the meteorological normalized reductions. The results showed around a 60% reduction in the NO2 at the three types of AQSs (traffic, suburban, and background) during the most restrictive months of the pandemic. The O3 concentration showed a different behavior depending on the type of AQS, pointing to changes in the regime of other pollutants, such as VOCs. The PM10 was the most difficult case to analyze because of its dependence on external transport phenomena, which were difficult to consider in the models. A set of CTM simulations should be done in the future to assess the O3-VOCs-NOx chemistry.

Non-linear models for black carbon exposure modelling using air pollution datasets

Black carbon (BC) is a product of incomplete combustion, present in urban aerosols and sourcing mainly from road traffic. Epidemiological evidence reports positive associations between BC and cardiovascular and respiratory disease. Despite this, BC is currently not regulated by the EU Air Quality Directive, and as a result BC data are not available in urban areas from reference air quality monitoring networks in many countries. To fill this gap, a machine learning approach is proposed to develop a BC proxy using air pollution datasets as an input. The proposed BC proxy is based on two machine learning models, support vector regression (SVR) and random forest (RF), using observations of particle mass and number concentrations (N), gaseous pollutants and meteorological variables as the input. Experimental data were collected from a reference station in Barcelona (Spain) over a 2-year period (2018–2019). Two months of additional data were available from a second urban site in Barcelona, for model validation. BC concentrations estimated by SVR showed a high degree of correlation with the measured BC concentrations (R2 = 0.828) with a relatively low error (RMSE = 0.48 μg/m3). Model performance was dependent on seasonality and time of the day, due to the influence of new particle formation events. When validated at the second station, performance indicators decreased (R2 = 0.633; RMSE = 1.19 μg/m3) due to the lack of N data and PM2.5 and the smaller size of the dataset (2 months). New particle formation events critically impacted model performance, suggesting that its application would be optimal in environments where traffic is the main source of ultrafine particles. Due to its flexibility, it is concluded that the model can act as a BC proxy, even based on EU-regulatory air quality parameters only, to complement experimental measurements for exposure assessment in urban areas.

Field calibration of a low-cost sensors network to assess traffic-related air pollution along the Brenner highway

This paper presents the results of a field campaign aiming at testing the ability of a network of low-cost electrochemical sensors to measure nitrogen dioxide concentration levels alongside one of the major Italian highway arteries. The results of a double on-field calibration, allowing for investigating the performance of the sensors under a broad range of weather conditions, are first shown and discussed. Different regression models are tested and their performance is widely assessed. Then, the measurements of the calibrated sensors are analyzed during a year-long field campaign, testing their performance against reference air quality stations and paying particular attention to different statistical indices. Results show a satisfactory performance of the low-cost sensors, highlighting their suitability to complement measurements from standard air quality stations, to reach a wider spatial coverage and to monitor pollutant concentrations in critical situations, when standard measurements are usually not feasible. Moreover, the dataset available from the year-long field campaign allows to extensively investigate nitrogen dioxide concentrations alongside the highway, pointing out in particular the strict relationship between pollutant concentration patterns and meteorological phenomena typical of Alpine valleys, such as daily-periodic thermally-driven wind systems.

Low-Cost Air Quality Stations’ Capability to Integrate Reference Stations in Particulate Matter Dynamics Assessment

Low-cost air quality stations can provide useful data that can offer a complete picture of urban air quality dynamics, especially when integrated with daily measurements from reference air quality stations. However, the success of such deployment depends on the measurement accuracy and the capability of resolving spatial and temporal gradients within a spatial domain. In this work, an ensemble of three low-cost stations named “AirQino” was deployed to monitor particulate matter (PM) concentrations over three different sites in an area affected by poor air quality conditions. Data of PM2.5 and PM10 concentrations were collected for about two years following a protocol based on field calibration and validation with a reference station. Results indicated that: (i) AirQino station measurements were accurate and stable during co-location periods over time (R2 = 0.5–0.83 and RMSE = 6.4–11.2 μg m−3; valid data: 87.7–95.7%), resolving current spatial and temporal gradients; (ii) spatial variability of anthropogenic emissions was mainly due to extensive use of wood for household heating; (iii) the high temporal resolution made it possible to detect time occurrence and strength of PM10 concentration peaks; (iv) the number of episodes above the 1-h threshold of 90 μg m−3 and their persistence were higher under urban and industrial sites compared to the rural area.

Investigation of Low-Cost and Optical Particulate Matter Sensors for Ambient Monitoring

This article presents a long-term evaluation of low-cost particulate matter (PM) sensors in a field measurements campaign. Evaluation was performed in two phases. During the first five months of the campaign, two PM sensors were simultaneously compared with the results from the reference air quality monitoring station in various atmospheric conditions—from the days with freezing cold (minimum temperature below −10 °C) and high relative humidity (up to 95%) to the days with the maximum temperature above 30 °C and low relative humidity (at the level of 25%). Based on the PM10 measurements, the correlation coefficients for both devices in relation to the reference station were determined (r = 0.91 and r = 0.94, respectively), as well as the impact of temperature and relative humidity on measurements from the low-cost sensors in relation to the reference values. The correction function was formulated based on this large set of low-cost PM10 measurements and referential values. The effectiveness of the corrective function was verified during the second measurement campaign carried out in the city of Nowy Sącz (located in southern Poland) for the same five months in the following year. The absolute values of the long-term percentage errors obtained after adjustment were reduced to a maximum of about 20%, and the average percentage errors were usually around 10%.

Development of a multiple regression model to calibrate a low-cost sensor considering reference measurements and meteorological parameters.

Low-cost air quality sensors are widely used to improve temporal and spatial resolution of air quality data. In Lima, Peru, only a limited number of reference air quality monitors have been installed, which has led to a lack of data for establishing environmental and health policies. Low-cost technology is promising for developing countries because it is small and inexpensive to operate and maintain. However, considerable work remains to be done to improve data quality. In this study, a low-cost sensor was installed with a reference monitor station as the first stage for the calibration process, and a multiple regression model was developed based on reference measurements as an outcome variable using sensor data, temperature, and relative humidity as the predictive parameters. The results show that this particular technology exhibits a promising performance in measuring PM2.5 and PM10 (particulate matter with diameter aerodynamic less than 2.5μm and 10μm, respectively); however, the correlation for PM2.5 appears to be better. Temperature and relative humidity data from the sensor were only partially analyzed due to the evident low correlation with the reference meteorological data. The objective of this study is to begin analyzing the performance of low-cost sensors that have already been introduced to the Peruvian market and selecting those that perform better to provide for informed decision-making.

Model Performance Differences in Fine-Mode Nitrate Aerosol during Wintertime over Japan in the J-STREAM Model Inter-Comparison Study

In this study, the results for nitrate (NO3−) aerosol during winter from the first-phase model inter-comparison study of Japan’s Study for Reference Air Quality Modeling (J-STREAM) were analyzed. To investigate the models’ external and internal settings, the results were limited to Community Multiscale Air Quality (CMAQ) models. All submitted models generally underestimated NO3− over the urban areas in Japan (e.g., Osaka, Nagoya, and Tokyo); however, some model settings showed distinct behavior. The differences due to the model external settings were larger than the model internal settings. Emissions were an important factor, and emissions configured with lower NOx emissions and higher NH3 emissions led to a higher NO3− concentration as the NH3 was consumed under NH3-rich conditions. The model internal settings of the chemical mechanisms caused differences over China, and this could affect western Japan; however, the difference over Tokyo was lower. To obtain a higher NO3− concentration over the urban areas in Japan, the selection of the HONO option for the heterogenous reaction and the inline calculation of photolysis was desired. For future studies, the external settings of the boundary condition and the meteorological field require further investigation.

Innovative low-cost air quality stations as a supporting means for road traffic regulations in urban areas

Air pollution is currently one of main issues affecting urbanized areas worldwide. Industrial activities, road traffic and heating systems are main emission sources significantly increasing the levels of atmospheric pollutants as particulate matter, ozone, and nitrogen oxides. Local administrations monitor these harmful gases by means of reference monitoring stations provided by regional/national environmental protection agencies. These stations, however, have limitations due to the little presence over the whole municipality, low time-frequency, and high costs. In this framework, CNR-IBE, University of Florence – Department of Agriculture, food, environment, and forestry (DAGRI), Tuscany Region Environmental Protection Agency (ARPAT) and epidemiologists of the Pisa University agreed an initiative to create an environmental “living lab” aimed at assessing the impacts due to anthropogenic activities on air quality and thus on population exposure. Two study areas located in the Tuscany region (Italy) were chosen: the rural town of Capannori, and the city of Florence. The town of Capannori was selected since it lies within a critical area both affected by a variety of emission sources and winter weather conditions unfavourable to pollutant dispersion. The city of Florence was chosen for assessing air quality in urban areas following a possible traffic reduction due to creation of new urban tramway lines. The air quality analysis was carried out by means of a monitoring network comprising innovative low-cost stations (named AIRQino). PM concentrations were mainly considered for providing indicative air quality measurements. The preliminary results indicated that: i) low-cost stations, after calibration and validation against more than one-year observations from a reference air quality station, confirmed their reliability in measuring air quality data; ii) AIRQino data can supplement air quality information from reference stations and may be used to help traffic regulation actions at urban scale.

Model Inter-Comparison for PM2.5 Components over urban Areas in Japan in the J-STREAM Framework

A model inter-comparison of secondary pollutant simulations over urban areas in Japan, the first phase of Japan’s study for reference air quality modeling (J-STREAM Phase I), was conducted using 32 model settings. Simulated hourly concentrations of nitric oxide (NO) and nitrogen dioxide (NO2), which are primary pollutant precursors of particulate matter with a diameter of 2.5 µm or less (PM2.5), showed good agreement with the observed concentrations, but most of the simulated hourly sulfur oxide (SO2) concentrations were much higher than the observations. Simulated concentrations of PM2.5 and its components were compared to daily observed concentrations by using the filter pack method at selected ambient air pollution monitoring stations (AAPMSs) for each season. In general, most models showed good agreement with the observed total PM2.5 mass concentration levels in each season and provided goal or criteria levels of model ensemble statistics in warmer seasons. The good performances of these models were associated with the simulated reproducibility of some dominant components, sulfates (SO42−) and ammonium (NH4+). The other simulated PM2.5 components, i.e., nitrates (NO3−), elemental carbon (EC), and organic carbon (OC), often show clear deviations from the observations. The considerable underestimations (approximately 30 µg/m3 for total PM2.5) of all participant models found on heavily polluted days with approximately 40–50 µg/m3 for total PM2.5 indicated some problems in the simulated local meteorology such as the atmospheric stability. This model inter-comparison suggests that these deviations may be owing to a need for further improvements both in the emission inventories and additional formation pathways in chemical transport models, and meteorological conditions also require improvement to simulate elevated atmospheric pollutants. Additional accumulated observations are likely needed to further evaluate the simulated concentrations and improve the model performance.

Field performance of a low-cost sensor in the monitoring of particulate matter in Santiago, Chile

Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10–PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9–24%) and PM10 (10–37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47–0.86) than PM10 (0.24–0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63–0.87 for continuous monitoring and 0.69–0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.

Identifying key factors influencing model performance on ground-level ozone over urban areas in Japan through model inter-comparisons

Ozone is one of remaining air quality issues in Japan. Effective strategies are required to reduce ozone at the ground level. Regional chemical transport models are useful in investigating relationships between precursor emissions and ambient concentrations of secondary pollutants including ozone. Model performance on ozone concentrations over urban areas in Japan were thoroughly evaluated in model inter-comparisons conducted in Japan's study for reference air quality modeling (J-STREAM). Specifically, 33 models with different configurations including the Community Multiscale Air Quality Modeling System (CMAQ), the Comprehensive Air Quality Model with Extensions (CAMx), and the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem) participated. They realized inter-comparisons with an unprecedented number of different model configurations. All the participating models overestimated ozone concentrations by 22 ± 4.6 ppb over urban areas in Japan during the summer. Spatial and temporal variations in model performance suggest the influence of background ozone concentrations. Based on differences in the participating model configurations, halogen chemistry and deposition, dry deposition velocity, precursor emissions in other countries, and vertical transport were identified as the key factors influencing simulated background ozone concentrations. Specific sensitivity analyses were conducted to evaluate the effects of the key influencing factors. Halogen chemistry and deposition implemented in recent versions of CMAQ caused more than 10 ppb reduction of simulated ozone over the ocean surrounding Japan, while the original dry deposition schemes used in CAMx and WRF-Chem, without any effect of halogen, can also cause larger dry deposition. Horizontal and downward transport of ozone kept in a residual layer over the continent can spread the effect of precursor emissions in other countries to downwind regions, including Japan. Differences in vertical transport can alter the spatial extent of their effects. It is essential to improve the influence of the aforementioned key factors to realize better model performance on ozone concentrations over urban areas, not only in Japan, but all over the world.

Differences in Model Performance and Source Sensitivities for Sulfate Aerosol Resulting from Updates of the Aqueous- and Gas-Phase Oxidation Pathways for a Winter Pollution Episode in Tokyo, Japan

During the Japanese intercomparison study, Japan’s Study for Reference Air Quality Modeling (J-STREAM), it was found that wintertime SO42– concentrations were underestimated over Japan with the Community Multiscale Air Quality (CMAQ) modeling system. Previously, following two development phases, model performance was improved by refining the Fe- and Mn-catalyzed oxidation pathways and by including an additional aqueous-phase pathway via NO2 oxidation. In a third phase, we examined a winter haze period in December 2016, involving a gas-phase oxidation pathway whereby three stabilized Criegee intermediates (SCI) were incorporated into the model. We also included options for a kinetic mass transfer aqueous-phase calculation. According to statistical analysis, simulations compared well with hourly SO42– observations in Tokyo. Source sensitivities for four domestic emission sources (transportation, stationary combustion, fugitive VOC, and agricultural NH3) were investigated. During the haze period, contributions from other sources (overseas and volcanic emissions) dominated, while domestic sources, including transportation and fuel combustion, played a role in enhancing SO42– concentrations around Tokyo Bay. Updating the aqueous phase metal catalyzed and NO2 oxidation pathways lead to increase contribution from other sources, and the additional gas phase SCI chemistry provided a link between fugitive VOC emission and SO42– concentration via changes in O3 concentration.

Application of a sensor network of low cost optical particle counters for assessing the impact of quarry emissions on its vicinity

The adverse health effects of inhaled particulate matter (PM) are global concern. Yet, in general, estimating the exposure to PM is challenging due to the sparsity of standard air quality monitoring stations and the low accuracy of dispersion models when high spatial resolution is required. The city of Elad, Israel, is situated less than 1 km from the Migdal Tzedek stone quarry, and public concerns were raised regarding the impact of the quarry's PM emissions on the city air quality.This work describes a year-long campaign of continuous measurements of particle number concentration (PNC) in few locations in the city and its vicinity, using a network of low cost optical particle counters (OPCs). To assess the possible impact of the quarry on the city we examined the OPCs' accuracy, coherency, and their capability to detect the quarry's impact on the PNC levels in the city. Using PNC time series in two size channels from a network of five nodes, PM10 and PM2.5 records from a nearby reference air quality monitoring station, and meteorological data, we could conclude that the quarry's impact on the city was small relative to the background PNC levels. Yet, more importantly, this work demonstrates the use of a network of low cost OPCs for responding to an environmental query for which the sparse standard air quality monitoring observations were not sufficient. The trade-offs between deployment of a network of cheap low-quality instruments and the use of a single high-end device are discussed.

Uncertainties in O3 concentrations simulated by CMAQ over Japan using four chemical mechanisms

Uncertainty was evaluated in four chemical mechanisms pertaining to O3 concentrations predicted over Japan by the Community Multiscale Air Quality Model (CMAQ) to investigate factors contributing to model overestimation of O3 concentration. The model setting and meteorological and emissions input data were obtained from a Japanese model inter-comparison project, Japan's Study for Reference Air Quality Modeling (J-STREAM). The compared gas-phase chemical mechanisms included the Carbon Bond Mechanism (CB05TUCL), Regional Atmospheric Chemical Mechanism (RACM2), and two mechanisms developed by the State Air Pollution Research Center (SAPRC), namely SAPRC07TC and SAPRC99. The O3 concentrations produced by CB05TUCL were low compared to those from SAPRC07TC. The RACM2 concentrations were similar to those from SAPRC07TC over inland Japan and lower over the sea. The concentrations from SAPRC99 were higher than those from SAPRC07TC in urban areas and lower in other areas. At most of the monitoring sites in Japan, the modeled O3 concentrations were higher than those from observations. Module overestimation can be ranked in the order of SAPRC99 > SAPRC07TC > RACM2 > CB05TUCL for urban sites and SAPRC07TC > SAPRC99 > RACM2 > CB05TUCL for rural sites. The concentration differences between the chemical mechanisms were within 10 ppb, whereas those between the observed and simulated O3 concentrations reached 40 ppb. Differences in O3 concentrations between the chemical mechanisms accounted for only a part of the model overestimation, while the rest remained unexplained. To investigate factors influencing the differences in O3 concentration between the chemical mechanisms, domain- and 10-vertical-layer-average hourly integrated process rates (IPRs) and integrated reaction rates (IRRs) were calculated using process analysis in CMAQ. The O3 chemical IPRs from SAPRC07TC were higher than those from CB05TUCL and RACM2. The SAPRC99 IPRs were higher than those from SAPRC07TC in urban areas and lower in other areas. The IRR differences in the chemical mechanisms showed that IRRs for the O3 and NO reactions were responsible for the differences in the O3 chemical IPR. The coefficients of determination between the O3 chemical process IPR and IRR differences in the chemical mechanisms were highest for the HO2-NO reaction in CB05TUCL and SAPRC99 and the RO2-NO reaction in RACM2. Differences in reaction rate constants and lumped volatile organic compounds may have caused some of the differences in O3 production between the chemical mechanisms.

Assessment of air quality microsensors versus reference methods: The EuNetAir Joint Exercise – Part II

The EuNetAir Joint Exercise focused on the evaluation and assessment of environmental gaseous, particulate matter (PM) and meteorological microsensors versus standard air quality reference methods through an experimental urban air quality monitoring campaign. This work presents the second part of the results, including evaluation of parameter dependencies, measurement uncertainty of sensors and the use of machine learning approaches to improve the abilities and limitations of sensors. The results confirm that the microsensor platforms, supported by post processing and data modelling tools, have considerable potential in new strategies for air quality control. In terms of pollutants, improved correlations were obtained between sensors and reference methods through calibration with machine learning techniques for CO (r2 = 0.13–0.83), NO2 (r2 = 0.24–0.93), O3 (r2 = 0.22–0.84), PM10 (r2 = 0.54–0.83), PM2.5 (r2 = 0.33–0.40) and SO2 (r2 = 0.49–0.84). Additionally, the analysis performed suggests the possibility of compliance with the data quality objectives (DQO) defined by the European Air Quality Directive (2008/50/EC) for indicative measurements.

Refinement of Modeled Aqueous-Phase Sulfate Production via the Fe- and Mn-Catalyzed Oxidation Pathway

We refined the aqueous-phase sulfate (SO42−) production in the state-of-the-art Community Multiscale Air Quality (CMAQ) model during the Japanese model inter-comparison project, known as Japan’s Study for Reference Air Quality Modeling (J-STREAM). In Japan, SO42− is the major component of PM2.5, and CMAQ reproduces the observed seasonal variation of SO42− with the summer maxima and winter minima. However, CMAQ underestimates the concentration during winter over Japan. Based on a review of the current modeling system, we identified a possible reason as being the inadequate aqueous-phase SO42− production by Fe- and Mn-catalyzed O2 oxidation. This is because these trace metals are not properly included in the Asian emission inventories. Fe and Mn observations over Japan showed that the model concentrations based on the latest Japanese emission inventory were substantially underestimated. Thus, we conducted sensitivity simulations where the modeled Fe and Mn concentrations were adjusted to the observed levels, the Fe and Mn solubilities were increased, and the oxidation rate constant was revised. Adjusting the concentration increased the SO42− concentration during winter, as did increasing the solubilities and revising the rate constant to consider pH dependencies. Statistical analysis showed that these sensitivity simulations improved model performance. The approach adopted in this study can partly improve model performance in terms of the underestimation of SO42− concentration during winter. From our findings, we demonstrated the importance of developing and evaluating trace metal emission inventories in Asia.