Monitoring Stations Research Articles

An overview of outdoor low-cost gas-phase air quality sensor deployments: current efforts, trends, and limitations

Abstract. We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. This study is not exhaustive of every gas-phase sensor network on the globe but rather exists to categorize types of sensor networks by their key characteristics and explore general trends. This also exposes gaps in monitoring efforts to date, especially regarding the availability of gas-phase measurements compared to particulate matter (PM) and geographic coverage gaps (the Global South, rural areas). We categorize ground-based networks that measure gas-phase air pollutants into two main subsets based on their deployment type – quasi-permanent (long term) and campaign (short to medium term) – and explore commonplace practices, strengths, and weaknesses of stationary monitoring networks. We conclude with a summary of cross-network unification and quality control efforts. This work aims to help scientists looking to build a sensor network explore best practices and common pathways and aid end users in finding low-cost sensor datasets that meet their needs.

Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake

Abstract. A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.

Clinical Study on the Induction of Psoriasis Flare-Ups by PM2.5 Air Pollutants via Immune Barrier Dysfunction.

This study explores the clinical correlation between the air pollutant PM2.5 and the induction of psoriasis flare-ups through the disruption of the immune barrier. Air quality data, the average mass concentration of the primary atmospheric pollutant PM2.5, and meteorological data spanning from November 2023 to March 2024 were gathered from 9 air quality monitoring stations situated within our city, courtesy of the local Meteorological Information Center. Psoriasis cases were sourced from our hospital's dermatology department, encompassing patients diagnosed and treated from November 2023 to March 2024 and residing within the city. From January 2023 to May 2024, aberrant expression of Th1 cells and Th2 cells was observed in psoriasis flare-up patients. Spearman correlation coefficient analysis revealed a positive correlation between PM2.5, PM10, and Th1 cells, and a negative correlation with Th2 cells, exhibiting significant differences (P < .05). Supplemental Figure 1 illustrates an increase in psoriasis flare-ups on the fifth day following a unit increase in PM2.5 concentration, with an excess risk (ER) value of 0.046 [95% confidence interval (CI): 0.137-0.893]. Conversely, after an increase of 1 unit of PM10, there was a decrease on the third and fourth days, followed by increases on the fifth, sixth, and seventh days, with ER values of 0.038 (95% CI: 0.013-0.067), 0.045 (95% CI: 0.019-0.073), 0.051 (95% CI: 0.034-0.078), 0.057 (95% CI: 0.045-0.083), and 0.061 (95% CI: 0.051-0.087), respectively. Air pollutant PM2.5 could potentially exacerbate psoriasis flare-ups by compromising the immune barrier, suggesting a plausible mechanism linked to the onset of this condition.

Indirect Value of Public Infrastructure Technology

Although prior research has primarily focused on the direct value of information technology (IT) and IT adoption by individuals and firms, this study explores the indirect value of IT in the form of public infrastructure technology. By exploiting a spatial discontinuity in water monitoring stations, we discover that firms located immediately upstream of water monitoring stations exhibit significantly lower levels of corruption than firms located immediately downstream. These findings are particularly noteworthy given that water monitoring stations have the potential to generate significant indirect value as they are not explicitly designed to mitigate corruption. Further analyses reveal that public infrastructure technology alone does not hold the key to mitigating corporate corruption. Instead, it is the synergistic interplay between public infrastructure technology and organizational change that drives the outcome. These findings contribute to a deeper understanding of the broader IT value landscape, emphasizing the indirect value of technological advancements in public infrastructure that were not originally intended for such benefit. Additionally, our findings highlight the benefits of leveraging existing infrastructure technology to address emerging societal needs. This paper was accepted by D. J. Wu, information systems. Funding: J. Liu acknowledges financial support from the National Natural Science Foundation of China [Grant 72402127], Shanghai Pujiang Program [Grant 23PJC059] and the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission [Grant 23CGA10]. S. Pei acknowledges financial support from the National Natural Science Foundation of China [Grant 72302139]. X. (M.) Zhang acknowledges financial support from the Hong Kong Research Grants Council [Grants GRF 14500521, GRF 14501320, GRF 165052947, and TRS:T31-604/18-N]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.01145 .

Impact of fine particulate matter (PM2.5) on ocular health among people living in Chiang Mai, Thailand

Considering the limited information on the impact of PM2.5 content on ocular health, a follow-up study was conducted on 50 healthy adults. Samples were collected twice, once before the PM2.5 exposure season and again after exposure. Daily PM2.5 concentration data was gathered from Thung Satok monitoring station. All subjects completed the self-structured ocular symptom questionnaire. The concentrations of 1-OHP were determined using HPLC-FLD. Logistic regression analysis investigated the relationship between PM2.5 toxicity and ocular symptoms. The findings revealed that daily PM2.5 concentrations surpassed the WHO-recommended range by around threefold. Exposure to PM2.5 significantly raised the likelihood of ocular redness (adjusted OR: 12.39, 95% CI), watering (adjusted OR: 2.56, 95% CI), and dryness (adjusted OR: 5.06, 95% CI). Additionally, these symptoms had an exposure-response relationship with increasing 1-OHP levels. Ocular symptoms worsened in frequency and severity during the high PM2.5 season, showing a strong link to elevated PM2.5 levels. Lymphocyte counts were also positively correlated with redness, watering, and dryness during high PM2.5 exposure. In conclusion, our study shows that subjects exposed to higher PM2.5 levels presented more significant ocular surface alterations.

Varying Performance of Low-Cost Sensors During Seasonal Smog Events in Moravian-Silesian Region

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and NO2 concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in PM10 readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating PM10 levels due to the prevalence of coarse particles. Calibration adjustments, based on the PM1/PM10 ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions.

Estimating monthly NO2, O3, and SO2 concentrations via an ensemble three-stage procedure with downscaled satellite remote sensing data and ground measurements

The gaseous air pollutants nitrogen dioxide (NO2), ozone (O3), and sulfur dioxide (SO2), in addition to particulate matter (PM2.5), are known to be associated with many adverse health effects. However, exposure estimates may not be available in rural or mountainous areas without monitoring stations. In this study, we retrieved satellite remote sensing data for NO2, O3, and SO2 from the Ozone Monitoring Instrument (OMI) L3 products. Together with ground measurements (air monitoring stations and meteorological and land use data), we estimated the monthly NO2, O3, and SO2 concentrations with a spatial resolution of 3×3 km across Taiwan from 2005 to 2019. A three-stage estimation procedure was utilized: in Stage 1, an ensemble generalized additive model (GAM) and machine learning method were used to determine the spatiotemporal variations; in Stage 2, the remote sensing data were downscaled; and in Stage 3, the downscaled concentrations were reused in the Stage 1 procedure for fine-tuning estimations. We obtained overall leave-one-out cross-validation (LOOCV) R2 values ranging from 0.927-0.950, 0.704-0.721, and 0.601-0.716, and root-mean-square-errors (RMSEs) ranging from 1.59-2.28, 3.81-4.18, and 0.67-1.32 ppb for NO2, O3, and SO2, respectively, via the random forest procedure. The annual NO2 and SO2 concentrations greatly improved from 2005-2019, especially in the western residential area of Taiwan. However, despite these improvements in air quality, the annual O3 concentrations tended to increase from 2015–2019. This might be due to the complex mixtures of precursors (e.g., NO2), atmospheric circulation, barriers of the Central Mountain Ridge, and increasing ground temperatures over the past decade. The proposed multistage estimation procedure performed well over the whole island with complex terrain and topography. The study outcomes may provide epidemiological information for long-term ambient exposure estimates and guidance for future administrative policies.

Design considerations for a continuous mussel farm in New England Offshore waters. Part I: Development of environmental conditions for engineering design

Sustainable aquaculture in nearshore waters faces challenges such as stakeholder conflicts, environmental pollution, and spatial constraints. Offshore aquaculture offers a promising solution but requires robust engineering design to withstand extreme weather conditions. This study develops the environmental conditions essential for engineering the design of a continuous mussel dropper system in New England offshore waters. A potential farm location was identified using criteria including water depth, federal boundaries, seafloor suitability, farm size, and proximity to ports, based on bathymetric and sedimentary maps. Historical data from five wave monitoring stations and two current velocity stations were analyzed to model extreme environmental conditions, as waves and currents pose primary threats. The extreme wave and current conditions are modeled using the Weibull distribution, on the annual maximum hourly significant wave height data and the largest 0.3% of current speeds. A newly proposed method combining Spalding’s wall function with a fourth-order polynomial is used to enhance the current profile analysis. Additionally, a joint probability density function was developed for wave height and current velocity at a specific depth, providing insights into wave height, period, and wavelength for various return periods such as 10, 25, 50, and 100 years. The results suggest a 10-yr wave of 8 m significant wave height and a current speed of 1.68 m/s, while a 50-yr values are 9.4 m and 1.96 m/s respectively. These findings offer critical data on extreme wave and current conditions in New England’s offshore waters, providing practical guidance for the engineering design of offshore mussel farms. This research advances offshore mussel farming and benefits the development of all types of offshore aquaculture systems.

Multiresolution Analysis of HRRR Meteorological Parameters and GOES-R AOD for Hourly PM2.5 Prediction.

High-resolution, comprehensive exposure data are crucial for accurately estimating the human health impact of PM2.5. In recent years, satellite remote sensing data have been increasingly utilized in PM2.5 models to overcome the limited spatial coverage of ground monitoring stations. However, data gaps in satellite-retrieved parameters such as aerosol optical depth (AOD), the sparsity of regulatory air quality monitors for model training, and nonlinear relationships between PM2.5 and meteorological conditions can affect model performance and cause data gaps in most existing PM2.5 models. In this study, spatial gaps in AOD obtained from Geostationary Operational Environmental Satellite-16 are filled using Goddard Earth Observing System Composition Forecasting AOD estimations. Furthermore, to improve model performance, meteorological predictors such as temperature from the High-Resolution Rapid Refresh model are preprocessed using Daubechies wavelet to extract low and high frequency components. The spatially gap-filled AOD, along with meteorological data, are ingested into a machine learning model to predict hourly PM2.5 at a 1 km spatial resolution in California. The model evaluation metrics (OOB (out-of-bag) R2 = 0.86 and RMSE (root-mean-square error) = 9.27 μg/m3 and 10-fold spatial cross-validation R2 = 0.82 and RMSE = 9.82 μg/m3) demonstrate the model's reliability in predicting ambient PM2.5, especially for states like California that experience frequent episodes of wildfires.

Improving PM10 sensor accuracy in urban areas through calibration in Timișoara

Low-cost particulate matter sensors (LCS) are vital for improving the spatial and temporal resolution of air quality data, supplementing sparsely placed official monitoring stations. Despite their benefits, LCS readings can be biased due to the physical properties of aerosol particles and device limitations. An optimization model is essential to enhance LCS data accuracy. This paper presents a calibration study of the LCS network of Timișoara, Romania. The calibration began by selecting LCS devices near National Air Quality Monitoring Network (NAQMN) stations and developing parametric models, choosing the best for broader application. Plantower, Sensirion, and Honeywell sensors showed comparable accuracy. Calibration involved clusters within a 750 m radius around NAQMN stations. Models incorporating RH corrections and multiple linear regression (MLR) were fitted. The best model was validated against data from unseen sensors, leading to mean bias errors (MBE) within 9-17% and RMSEs of 33-35%, within sensor uncertainty margins. Applied to the city-wide LCS network, the model identified several stations regularly exceeding the EU daily PM10 threshold, unnoticed by NAQMN stations due to their limited coverage. The study highlights the necessity of granular monitoring to accurately capture urban air quality variations.

Evaluation of Machine Learning Application on the Prediction of Particulate Matter Concentrations in Small/Medium-Sized City

Objectives:In this study, Machine Learning (ML) algorithms were evaluated to predict the concentration of particulate matter (PM10 and PM2.5) using air quality and meteorological data in small/medium-sized city.Methods:ML models, including Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Random Forest (RF), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), were used to predict PM10 and PM2.5 concentrations. Five air quality variables, including NO2, SO2, CO, PM10 and PM2.5, and seven meteorological variables, including temperature, humidity, vapor pressure, wind speed, precipitation, local atmospheric pressure, and sea-level atmosphere pressure, were collected from three air quality monitoring stations and one meteorological observatory from 2017 to 2022. A total of 52,583 sets of data were used for ML. The prediction accuracies of the applied ML models were evaluated using the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of determination (R2).Results and Discussion:Higher ML performance was obtained when using the data including PM10 and PM2.5 compared to the data excluding these variables. Among five different ML models, the XGB model showed the highest accuracy in predicting PM10 and PM2.5 one hour in the future. However, poorer performance was obtained as the predicted period increased from one hour to 72 hours.Conclusion:The application of ML algorithms for the short-term prediction of PM10 and PM2.5 was successful in this study. However, more input variables and deep learning algorithms are needed for long-term prediction of PM10 and PM2.5.

Comparative analysis of airborne fungal spore distribution in urban and rural environments of Slovakia.

Monitoring airborne fungal spores is crucial for public health and plant production since they belong to important aeroallergens and phytopathogens. Due to different land use, their concentration can differ significantly between urban and rural areas. We monitored their spectrum and quantity on two geographically close sites with a different degree of urbanisation: Bratislava City and Kaplna Village in Slovakia, located 38km apart. We recorded the spectrum of airborne fungal spores over a year and confirmed the microscopic results by amplicon-based metagenomic analysis. The main spore season of the most frequent genera lasted over a week longer in Kaplna, but its intensity was approximately two-fold higher in Bratislava. This can be possibly connected to the microclimatic conditions of the urban area (especially wind speed and heat island effect) and the lesser use of fungicides. Cladosporium was the dominant genus on both sites, influencing the intensity most significantly. Through statistical analysis of the influence of meteorological parameters on airborne fungal spore levels, we identified a significant relationship with temperature, while the impact of other parameters varied depending on the spore type and release mechanism. Our results show the differences in airborne fungal spore levels between urban and rural areas and highlight the necessity for more monitoring stations in various environments.

Deriving water quality index using site-specific water quality parameter guidelines for real-time water quality stations

ABSTRACT The Newfoundland and Labrador real-time water quality (RTWQ) monitoring program operates a network of surface water monitoring stations, measuring physical water quality parameters in near real time. The network generates vast amounts of data that are too complex for many stakeholders to interpret. While the Canadian Council of Ministers of the Environment water quality index (WQI) has been a valuable tool to simplify and summarize the ambient quality of water, its usage in RTWQ is virtually non-existent. This is largely due to the lack of alignment/congruency between the limited number of parameters from real-time networks and the availability of aquatic water quality guidelines. This paper is the first attempt to outline a method of utilizing real-time water quality data to generate site-specific water quality guidelines using the background concentration method. Using these guidelines, we compute weekly WQI scores for select monitoring stations and examine various influences on the scores, including precipitation. The influence of each parameter on the WQI score is also plotted in the computation of the RTWQ WQI. Comparison is made between the yearly scores of the Canadian Environmental Sustainability Indicators WQI and RTWQ WQI. The paper also discusses the challenges and benefits of using this methodology as well as the sensitivity of WQI scoring at stations with various anthropogenic influences.

Complex assessment of ambient particulate matter in a small settlement with coal-handling port

Relevance. Russian coal exports have been steadily increasing in recent years, and Far Eastern ports, originally not adapted to dusty cargoes, have switched to coal operations. Unoptimized coal handling leads to increased content of coal-containing particles in the air and to various environmental problems. Aim. To analyze the concentration of particulate matter in the air and the chemical composition of particles on the surface of conifer needles in the Posyet settlement (Primorsky Krai, Russia), where a large coal terminal operates. Materials and methods. The chemical composition was studied on the surface of conifer needles by X-ray fluorescence and in washout of conifer needle samples using Raman spectroscopy and energy dispersive analysis. Concentrations of particles with the diameter of 2.5 and 10 μm (PM2.5 and PM10, respectively) were measured using automatic monitoring stations. Results and conclusions. Chemical composition was dominated by mineral particles (calcite, silicates, etc.). However, the presence of coal particles (up to 8.3%) and metal particles was also observed. Mean concentrations of PM2.5 and PM10 were within the Russian and international health standards, but close to their upper limits. Concentrations of both measured particle fractions were highest during the winter months (as well as in March, which is traditionally a cold month in the Far East), followed by a gradual decrease. The study obtained new data on the annual variations of concentrations of PM2.5 and PM10, as well as on the basic composition of particles from the surface of woody vegetation (needles) as an indicator of the state of air pollution in this settlement.

Tropopause Height Inferred from Twilight Photometry

Abstract The tropopause height has been measured by sounding balloons for more than 70 years. More recently, lidar has found the tropopause height by detecting aerosols or subvisible cirrus that accumulate at the tropopause. Here we present a twilight photometer technique to detect the tropopause height with results close to the traditional World Meteorological Organization (WMO) method from sounding balloon measurements. The twilight intensity (I), although radiometrically uncalibrated, can be normalized by the derivative of log(I) with respect to time (t), or I’(t)/I(t), to infer stratospheric aerosol layer properties using Earth’s shadow to orderly shield the sunlight below different altitudes. A conventional twilight photometer detects the twilight glow from atmospheric aerosol scattering with a photodiode detector equipped with a near-infrared optical filter. We have replaced the photodiode and optical filter with a Light-Emitting Diode (LED) that functions as a wavelength-dependent photodiode with a narrow-band response near 1050 nm. We found that the layer height from twilight photometer observations is highly correlated with the tropopause height. 76 twilight tropopause heights during the first six months of 2024 at Geronimo Creek Atmospheric Monitoring Station (GCAMS) in Central Texas agree well with those reported by sounding balloons launched from Corpus Christi, Texas, 207 km from GCAMS (standard deviation of 265 m). The twilight method is inexpensive, easily implemented, and has potential for use at remote sites with solar power and developing countries that lack resources for sounding balloons. It can also provide a learning tool for students of meteorology and the atmospheric sciences.

Data-driven optimization of coastal sea level monitoring: Leveraging known patterns for enhanced reconstruction

Efficiently configuring sea level monitoring stations is crucial for obtaining accurate spatiotemporal data while managing operational and maintenance costs and addressing the challenges posed by missing data. This study focuses on optimizing the selection of stations within Turkey's coastal sea level monitoring network by leveraging the inherent lower dimensionality in data. The network consists of 18 stations distributed along Turkey's coastline. To identify dominant patterns in historical sea level data, Empirical Orthogonal Function (EOF) analysis was employed, followed by the application of the QR decomposition with column pivoting algorithm. Model performance is assessed using the Nash-Sutcliffe coefficient of efficiency (CE) and root mean square error (RMSE). Remarkably, the results demonstrated that reconstructing the entire dataset, encompassing all 18 stations was possible with a CE of 0.94 and an RMSE of 0.06. Even utilizing data from two or three stations alone achieves acceptable reconstruction accuracy. The effectiveness of EOF analysis combined with QR with column pivoting algorithm suggests promising applications in various scientific fields.

Dissolved Oxygen Concentration Prediction in the Pearl River Estuary with Deep Learning for Driving Factors Identification: Temperature, pH, Conductivity, and Ammonia Nitrogen

Predicting the dissolved oxygen concentration and identifying its driving factors are essential for improved prevention and management of anoxia in estuaries. However, complex hydrodynamic conditions and the limitations in traditional methods result in challenges in the identification of the driving factors for the low dissolved oxygen (DO) phenomenon. The objective of our study is to develop a robust deep learning model using four-year in situ data collected from an automatic water quality monitoring station (AWQMS) in an estuary, for accurate identification and quantification of the driving factors influencing DO levels. Mitigations in hypoxia were observed during the initial two years, but a subsequent decline in DO concentrations was witnessed recently. The periodicity of DO concentrations in the Pearl River Estuary reduced with the increase in the hypoxic intensity. Maximal information coefficient (MIC) and extreme gradient boosting (XGBoost) were employed to determine the significance of input variables, which were subsequently validated by using the long- and short-term memory networks (LSTMs). The driving factors contributing to the hypoxia problem were shown as temperature, pH, conductivity, and NH4+-N concentrations. Notably, the evaluation index values of the hybrid model are MAPE = 0.0887 and R2 = 0.9208, which have been improved compared with the LSTM model by about 99.34% in MAPE reduction and 16.56% in R2 improvement, indicating that the MixUp-LSTM model was capable of effectively capturing nonlinear relationships between DO and other water quality indicators. Based on existing literature, three traditional statistical methods and four machine learning models were also performed to compare with the proposed MixUp-LSTM model, which outperformed other models in terms of prediction accuracy and robustness. Overall, the successful identification of the driving factors for the deoxygenation phenomenon would have important implications for the governance and regulation of low DO in estuaries.

Investigating uncertainties in air quality models used in GMAP/SIJAQ 2021 field campaign: General performance of different models and ensemble results

The international field campaign, GMAP/SIJAQ 2021, was conducted in Korea from October 18th to November 25th to enhance the performance and validation of the Geostationary Environment Monitoring Spectrometer (GEMS) products algorithm and obtain a better understanding of the current air pollution status of the Korean Peninsula. Five chemical transport models (CTMs), including CMAQ, CMAQ-GIST, CAMx, WRF-Chem, and WRF GEOS-Chem, were utilized during the campaign to assist in organizing the observation plan and identifying changes in pollutant concentrations and their spatiotemporal distribution in Korea following the Korea–United States Air Quality (KORUS-AQ) 2016. In this study, we evaluated the forecasting performance, strengths, and limitations of these five CTMs and their ensemble in simulating air quality. Intensive measurement data and intercomparisons were employed to explain discrepancies between observed and simulated results. A comparison of the CTM ensemble results for PM2.5 and various gaseous pollutants between the current GMAP/SIJAQ 2021 and previous KORUS-AQ 2016 campaigns showed the R-value for the total mass PM2.5 concentration increased from 0.88 to 0.94. This improvement is related to CTM updates, including the emission inventory and better reproductions of the concentrations of gaseous species. However, the models consistently underestimated carbon monoxide (CO) concentrations, similar to the results from KORUS-AQ. This finding still suggests a further challenge that requires consideration of missing anthropogenic sources. The results of the ensemble model agreed well with the chemical composition of PM2.5 observed at the intensive monitoring station. However, for NO3− and NH4+, discrepancies were primarily due to inaccuracies in the meteorological inputs, such as precipitation, relative humidity (RH), and nighttime planetary boundary layer height (PBLH) in the CTMs. Hence, all models overestimated the concentration of elemental carbon (EC), therefore, it is necessary to revise EC emissions in the SIJAQv2 inventory, as these apply to unusual levels recorded in Seoul during the reference year of 2018.

A selective diesel vehicle ban’s impact on NO2 concentrations in Munich: a quasi-experimental study

Abstract Background The European Union’s current NO2 concentration limit of 40 μg/m³ is regularly exceeded in Munich, Germany. This limit is likely to be reduced towards the WHO recommended target of 10 μg/m³. In response, the city enacted a selective ban on older diesel vehicles, specifically those categorized as Euro 4 or older, within the current low-emission zone in February 2023. Our study examined how Munich’s selective ban on diesel vehicles impacted NO2 levels, concentrating on the six-month period after its introduction. Methods We employed a synthetic control approach in the primary analysis and a controlled interrupted time series approach in the secondary analysis. These quasi-experimental methods establish a ‘counterfactual’ no-intervention scenario, allowing for comparison with the actual observed scenario and estimation of the impact of the intervention. Historical data from monitoring stations within and outside the low-emission zone from 2014 to 2022 were utilized as controls, while potential confounders were considered. Results NO2 concentrations within Munich’s low-emission zone decreased across the study period. Effects of the selective diesel vehicle ban at monitoring stations within the low-emission zone were minimal, with wide confidence intervals indicating uncertainty. At Landshuter Allee, the average intervention effect was -2.67 μg/m3 (95%-CI = [-12.72; 7.38]), at Stachus it was -2.74 μg/m3 (95%-CI = [-9.91; 4.42]) and at Lothstrasse, it was -1.03 μg/m3 (95%-CI = [-7.75; 5.69]). The secondary analysis confirms these findings. Conclusions Munich’s selective diesel vehicle ban had limited effects in reducing NO2 concentrations. Factors such as its narrow focus on Euro 4 diesel vehicles, numerous exemptions, and unclear enforcement may explain this outcome. Comprehensive strategies and continuous monitoring and evaluation are crucial for effectively addressing urban air pollution and protecting public health. Key messages • Munich’s selective diesel vehicle ban had minimal effects on reducing NO2 levels, possibly due to its narrow scope, many exemptions, and unclear enforcement. • Current guidelines and court rulings signal the need for more effective solutions beyond selective bans, emphasizing the importance of well-designed interventions and rigorous evaluation.

Development of empirical models for calculating global and diffuse erythemal weighted solar ultraviolet radiation under clear sky conditions in Thailand

This study introduced semi-empirical models for calculating hourly global and diffuse erythemal weighted ultraviolet (EUV) solar radiation under clear sky conditions in Thailand. To develop these models, global and diffuse erythemal ultraviolet data collected over a decade (2011–2020) from four solar monitoring stations situated in the main regions of Thailand were used. The data was classified into two groups. The first group (2011–2018) was used for modeling, while the second group (2019–2020) was reserved for model validation. The global and diffuse EUV radiation models revolve around semi-empirical functions. These functions express both types of EUV radiation in terms of normalized variables, specifically the total ozone column, aerosol optical depth, and air mass. To assess the accuracy of the proposed models, their outputs for hourly global and diffuse EUV radiation were calculated at the four monitoring stations. These outputs were then compared against actual measurements to validate the effectiveness of the models. The evaluation revealed a root mean square difference of 15.8% for global EUV radiation and 14.9% for diffuse EUV radiation when compared to the mean measured values.