Dense Monitoring Network Research Articles

Leveraging low-cost sensors to predict nitrogen dioxide for epidemiologic exposure assessment.

Statistical models of air pollution enable intra-urban characterization of pollutant concentrations, benefiting exposure assessment for environmental epidemiology. The new generation of low-cost sensors facilitate the deployment of dense monitoring networks and can potentially be used to improve intra-urban models of air pollution. Develop and evaluate a spatiotemporal model for nitrogen dioxide (NO2) in the Puget Sound region of WA, USA for the Adult Changes in Thought Air Pollution (ACT-AP) study and assess the contribution of low-cost sensor data to the model's performance through cross-validation. We developed a spatiotemporal NO2 model for the study region incorporating data from 11 agency locations, 364 supplementary monitoring locations, and 117 low-cost sensor (LCS) locations for the 1996-2020 time period. Model features included long-term time trends and dimension-reduced land use regression. We evaluated the contribution of LCS network data by comparing models fit with and without sensor data using cross-validated (CV) summary performance statistics. The best performing model had one time trend and geographic covariates summarized into three partial least squares components. The model, fit with LCS data, performed as well as other recent studies (agency cross-validation: CV- root mean square error (RMSE) = 2.5 ppb NO2; CV- coefficient of determination ( ) = 0.85). Predictions of NO2 concentrations developed with LCS were higher at residential locations compared to a model without LCS, especially in recent years. While LCS did not provide a strong performance gain at agency sites (CV-RMSE = 2.8 ppb NO2; CV- = 0.82 without LCS), at residential locations, the improvement was substantial, with RMSE = 3.8 ppb NO2 and = 0.08 (without LCS), compared to CV-RMSE = 2.8 ppb NO2 and CV- = 0.51 (with LCS). We developed a spatiotemporal model for nitrogen dioxide (NO2) pollution in Washington's Puget Sound region for epidemiologic exposure assessment for the Adult Changes in Thought Air Pollution study. We examined the impact of including low-cost sensor data in the NO2 model and found the additional spatial information the sensors provided predicted NO2 concentrations that were higher than without low-cost sensors, particularly in recent years. We did not observe a clear, substantial improvement in cross-validation performance over a similar model fit without low-cost sensor data; however, the prediction improvement with low-cost sensors at residential locations was substantial. The performance gains from low-cost sensors may have been attenuated due to spatial information provided by other supplementary monitoring data.

Improved daily estimates of relative humidity at high resolution across Germany: A random forest approach

The lack of readily available methods for estimating high-resolution near-surface relative humidity (RH) and the incapability of weather stations to fully capture the spatiotemporal variability can lead to exposure misclassification in studies of environmental epidemiology. We therefore aimed to predict German-wide 1 × 1 km daily mean RH during 2000–2021. RH observations, longitude and latitude, modelled air temperature, precipitation and wind speed as well as remote sensing information on topographic elevation, vegetation, and the true color band composite were incorporated in a Random Forest (RF) model, in addition to date for capturing the temporal variations of the response-explanatory variables relationship. The model achieved high accuracy (R2 = 0.83) and low errors (Root Mean Square Error (RMSE) of 5.07%, Mean Absolute Percentage Error (MAPE) of 5.19% and Mean Percentage Error (MPE) of - 0.53%), calculated via ten-fold cross-validation. A comparison of our RH predictions with measurements from a dense monitoring network in the city of Augsburg, South Germany confirmed the good performance (R2 ≥ 0.86, RMSE ≤ 5.45%, MAPE ≤ 5.59%, MPE ≤ 3.11%). The model displayed high German-wide RH (22y-average of 79.00%) and high spatial variability across the country, exceeding 12% on yearly averages. Our findings indicate that the proposed RF model is suitable for estimating RH for a whole country in high-resolution and provide a reliable RH dataset for epidemiological analyses and other environmental research purposes.

Two-Stage Fuzzy Traffic Congestion Detector

This paper presents a two-stage fuzzy-logic application based on the Mamdani inference method to classify the observed road traffic conditions. It was tested using real data extracted from the Padua–Venice motorway in Italy, which contains a dense monitoring network that provides continuous measurements of flow, occupancy, and speed. The data collected indicate that the traffic flow characteristics of the road network are highly perturbed in oversaturated conditions, suggesting that a fuzzy approach might be more convenient than a deterministic one. Furthermore, since drivers have a vague notion of the traffic state, the fuzzy method seems more appropriate than the deterministic one for providing drivers with qualitative information about current traffic conditions. In the proposed method, the traffic states are analysed for each road section by relating them to average speed values modelled with fuzzy rules. An application using real data was carried out in Simulink MATLAB. The empirical results show that the proposed study performs well in estimation and classification.

Horizontal seismic wave at ground surface from transfer function based on ambient noise

Earthquake detection can be improved by ensuring that seismometer sites experience little artificial noise in the surrounding environment. To minimize noise, seismological stations should be positioned in rocky mountainous areas without nearby valleys, away from significant human activity. However, such surface sites may be scarce when constructing dense monitoring networks, necessitating the use of underground sites to ensure low noise levels. The Korean Meteorological Administration is currently installing new underground seismometers to increase seismic monitoring capacity. However, seismic data on the ground surface are also required for engineering technological developments (to reduce damage to structural components). Therefore, borehole seismic stations without surface seismometers need to estimate ground surface motion from borehole record data. We propose a transfer function that converts motion within boreholes to surface seismic waves using ambient noise, thereby facilitating estimation of ground surface motions using borehole seismometers. As a result, predicting ground surface motion from borehole record data becomes possible.

Improving the performance of portable aerosol size spectrometers for building dense monitoring networks

A new charging method is deployed to improve the accuracy of portable size spectrometers with reduced size and maintenance, thus, more suitable for building dense monitoring networks.

Spatiotemporally resolved daily relative humidity predictions across Germany during 2000-2021: a Random Forest approach

BACKGROUND AND AIM The lack of high-resolution relative humidity (RH) datasets and the limited ability of available weather stations to fully capture the spatiotemporal RH variability might lead to errors in exposure assessment for epidemiological studies and biased health effects estimates. We aimed to predict German-wide 1×1 km daily mean RH during 2000-2021 by using a machine learning approach. METHODS We used data from multiple sources, including RH observations, modelled precipitation and wind speed as well as remote sensing elevation, vegetation and the visible red, green and blue light bands. Our main predictor for estimating RH was modelled daily mean air temperature that we previously estimated in 1×1 km across Germany through a multi-stage regression-based approach incorporating two linear mixed models. Additionally, we included date in our RH model, capturing the day-to-day variation of the response-explanatory variables relationship. All the aforementioned predictors were included in a Random Forest (RF) model, which was separately developed for each year. RESULTS Ten-fold cross validation showed that the RF model achieved high accuracy (R²=0.81) and low errors (Root Mean Square Error (RMSE)=5.26%). We also compared our output with an independent dataset from a dense monitoring network in Augsburg metropolitan area (R²=0.81, RMSE=5.42%). Our models displayed high RH overall (21y-average RH=78.8%) and high spatial variability within a year across the country, exceeding 12.1% on average. RH distribution followed spatial patterns including urbanization, mountains, rivers and coastlines. For instance, the Alps and the North Sea coast were areas with elevated RH, while extended urban cores or individual cities were less humid than the surrounding rural settings. CONCLUSIONS Our findings indicate the proposed RF model as suitable for countrywide RH modeling at high spatiotemporal resolution, providing a reliable dataset for subsequent epidemiological analyses and other research purposes. KEYWORDS: relative humidity, spatiotemporal modeling, random forest, exposure assessment

Estimating Static, Dynamic and Monsoon Groundwater Reserves in a Dense Well Network of Basaltic Terrain, Central India

Abstract Water is an essential resource for life. Effective management and sustainable development of this limited resource have become difficult not only in India but also in the world. Groundwater is a replenishable resource, but its availability is a heterogeneous distribution of complex hydrogeological terrain in both space and time. In this article, the groundwater reserves was estimated in a dense well network of basaltic terrain (area:360 km2) in central India for both regional and local scales. Groundwater level data were collected from four observation wells at a regional scale, which in general, were used to assess groundwater reserves, and also from a dense 58 well network at a local scale. These data were utilized for groundwater reserves at each well site in terms of static groundwater reserve (SGWR), dynamic groundwater reserve (DGWR), and monsoon groundwater reserve (MGWR). Their spatial and temporal variabilities were also compared with the help of Thiessen polygons. Results show that the SGWR and DGWR of the dense monitoring network range from 66.7 to 87.13 MCM, with an average of 75.77 MCM, and from 34.83-48.17 MCM, with an average of 41.95 MCM, respectively. Whereas, the MGWR varies from 36.81 to 55.08 MCM, with an average of 44.92 MCM. The comparison in the reserves of the regional scale is found to be higher than the dense well network data. This outcome could be useful for the implementation of groundwater development and sustainable management in this basaltic area in future.

Muography for a dense tide monitoring network

Sub-hourly to seasonal and interannual oceanographic phenomena can be better understood with high special resolution and high frequency tidal observations. However, while current tidal measurements can provide sufficiently high observational density in terms of time, the observational density in terms of space is low mainly due to the high expense of constructing tide gauge stations. In this work, we designed a novel tide monitoring technique with muography that could be operated in near-shore basements (or similar structures on land below sea level) and found that more practical, stable, robust and cost-effective high-spatiotemporal-density tide measurements are possible. Although the time resolution, sensitivity, and the distance between the detectors and the shorelines are tradeoffs, hourly and annual sensitivity (ability to detect the tide height variations) of less than 10 cm and 1 mm can be statistically attained, respectively. It is anticipated that the current muographic technique could be applied as an alternative, cost-effective and convenient dense tidal monitor network strategy in coastal areas worldwide.

Assessment of natural groundwater reserve of a morphodynamic system using an information-based model in a part of Ganga basin, Northern India

Assessment of morphodynamic groundwater reserves is important for the sustainable management of water resources. It is a truth that groundwater resource evaluation is anxious with the ambiguity of its several factors and employing methods. Thus, an information-based model has been hypothesized to assess natural groundwater reserves in a morphodynamic system in a part of the Ganga basin of Northern India, where the spatial variability in natural groundwater reserve exists. Marginal information of rainfall data, and transinformation among the rainfall, and monthly depth to groundwater level measurement at 50 wells in a dense monitoring network were used for evaluating natural groundwater reserve. The results indicate that an average recharge rate is about 246 mm/monsoon and or 32.65% of the seasonal rainfall, and its values are well-correlated with the soil infiltration rate. It has been found that the estimated recharge rates are about 54.08, 45.85, 33.77, 32.48, and 32.14% of the seasonal rainfall in an active flood plain, back swamp, natural levees, flood plain, and palaeochannel, respectively. The calculated annual rainfall input to groundwater reserve is found about 127.98 MCM/monsoon rainfall, which could be employed for sustainable management of groundwater resources in the morphodynamic system of the Ganga river basin.

An Exponential Filter Model-Based Root-Zone Soil Moisture Estimation Methodology from Multiple Datasets

Modern smart agriculture initiative presents more requests for soil moisture (SM) monitoring over large agricultural areas. Remote sensing techniques facilitate high-resolution surface SM (SSM) estimation at a large scale but lack root zone SM (RZSM) information. Establishing the deduction method of RZSM from the SSM has long been the focus of most attention. Data assimilation methods are promising techniques for RZSM estimation, developing numerous assimilated reanalysis datasets, e.g., ERA5 and the latest Soil Moisture Active and Passive (SMAP) L4 SM product. However, data latency and large computation during data collecting and processing often inhibits further applications. This work proposes a rapid estimation scheme for estimating RZSM with short latency and small computations, based on the Exponential Filter (EF) method. The EF model with single parameter T was firstly calibrated and validated using the SSM and RZSM of ERA5 reanalysis dataset, obtaining the optimum parameter T map for each grid. Then, the fast-updating SMAP L3 SSM product together with the scale-matched optimum T were adopted as inputs into the EF model to retrieve RZSM estimation of each grid. Specifically, such estimation scheme was tested over the central and eastern agricultural areas of China, using a dense monitoring network of 796 SM observation sites, which contains various land uses, as well as meteorological and hydrological conditions. The calibrated optimum parameter T presented an increasing trend with good physical explanations. Furthermore, all the estimated RZSMs were found to have good performances on capturing the temporal-spatial variations of RZSM and well reflecting seasonal RZSM changes. Overall, such an estimation scheme was proven to be a desirable alternative for estimating RZSM over large agricultural areas.

From air quality sensors to sensor networks: Things we need to learn

As a potential complement to traditional regulatory instruments, low-cost air quality sensors (LCAQS) can be deployed in dense monitoring networks to provide timely and comprehensive snapshots of pollutant concentrations and their spatial and temporal variability at various scales with relatively less cost and labor. However, a lack of practical guidance and a limited understanding of sensor data quality hinder the widespread application of this emerging technology. We leveraged air quality data collected from state and local monitoring agencies in metropolitan areas of the United States to evaluate how low-cost sensors could be deployed across the U.S. We found that ozone, as a secondary pollutant, is more homogeneous than other pollutants at various scales. PM2.5, CO, and NO2 displayed homogeneities that varied by city, making it challenging to design a uniform network that was suitable across geographies. Our low-cost sensor data in New York City indicated that PM2.5 sensors track well with light-scattering reference methods, particularly at low concentrations. The same phenomenon was also found after thoroughly evaluating sensor evaluation reports from the Air Quality Sensor Performance Evaluation Center (AQ-SPEC). Furthermore, LCAQS data collected during wildfire episodes in Portland, OR show that a real-time (i.e. in situ) machine learning calibration process is a promising approach to address the data quality challenges persisting in LCAQS applications. Our research highlights the urgency and importance of practical guidance for deploying LCAQS.

Exploring Sources of Surface Bias in HRRR Using New York State Mesonet

AbstractIn recent years, there has been increasing demand for applications of short‐term forecasting of renewable energy potential and assessments of the likelihood of extreme weather events using the High‐Resolution Rapid Refresh (HRRR) model. Examining the biases in the newest version of HRRR is necessary to promote further model development. Using data from one of the most comprehensive and dense monitoring networks, New York State Mesonet (NYSM), we evaluate the HRRR version 3 meteorological fields for an entire year. In this work, the land‐atmosphere‐cloud coupling system is evaluated as an integrated whole. We investigate the physical processes influencing the soil hydrological balance and the thermodynamic interactions, from surface fluxes up to the level of boundary layer convection from both temporal (seasonal and diurnal) and spatial perspectives. Results show that the model 2 m temperature and humidity biases are seasonally dependent, with warm and dry bias present during the warm season, and an extreme nocturnal cold bias in winter. The summer warm bias includes both a land‐surface‐induced bias and a cloud‐induced bias. Inaccurate representation of energy partition and soil hydrological process across different land use types as well as a hydrological bias in describing spring snowmelt are identified as the main source of the land‐surface‐induced bias. A feedback loop linking cloud presence, flux changes, and temperature contributes to the cloud‐induced bias. The positive solar radiation bias increases from clear sky to overcast sky conditions. The most significant bias occurs during overcast and thick cloud conditions associated with frontal passage and thunderstorms.

Imaging the Structure and the Saltwater Intrusion Extent of the Luy River Coastal Aquifer (Binh Thuan, Vietnam) Using Electrical Resistivity Tomography

With the growing population and the adverse effects of climate change, the pressure on coastal aquifers is increasing, leading to a larger risk of saltwater intrusion (SI). SI is often complex and difficult to characterize from well data only. In this context, electrical resistivity tomography (ERT) can provide high-resolution qualitative information on the lateral and vertical distribution of salinity. However, the quantitative interpretation of ERT remains difficult because of the uncertainty of petrophysical relationships, the limitations of inversion, and the heterogeneity of aquifers. In this contribution, we propose a methodology for the semiquantitative interpretation of ERT when colocated well data are not available. We first use existing wells to identify freshwater zones and characterize the resistivity response of clayey deposits. Then, we approximate the formation factor from water samples collected in the vicinity of ERT data to derive a resistivity threshold to interpret the saline boundary. We applied the methodology in the shallow aquifers of the Luy River in the Binh Thuan province, Vietnam, where water resources are under pressure due to agricultural, aquacultural, and industrial production. Twenty-one ERT profiles were collected and revealed a much larger intrusion zone, compared to the previous study. Saltwater is present in lowland areas of the left bank over almost the whole thickness of the aquifer, while the right bank is constituted of sand dunes that are filled with freshwater. At a larger distance from the sea, a complex distribution between fresh and saltwater is observed. Our methodology could be applied to other heterogeneous aquifers in the absence of a dense monitoring network.

Constraining emission estimates of carbon monoxide using a perturbed emissions ensemble with observations: a focus on Beijing

The reliability of air quality simulations has a strong dependence on the input emissions inventories, which are associated with various sources of uncertainties, particularly in regions undergoing rapid emission changes where inventories can be ‘out of date’ almost as soon as they are compiled. This work provides a new methodology for updating emissions inventories by source sector using air quality ensemble simulations and observations from a dense monitoring network. It is adopted to determine the short-term trends in carbon monoxide (CO) emissions, an important pollutant and precursor to tropospheric ozone, in a study area centred around Beijing following the implementation of clean air policies. We sample the uncertainties associated with using an a priori emissions inventory for the year 2013 in air quality simulations of 2016, using an atmospheric dispersion model combined with a perturbed emissions ensemble (PEE), which is constructed based on expert-elicited uncertainty ranges for individual source sectors in the inventory. By comparing the simulation outputs with observational constraints, we are able to constrain the emissions of key source sectors relative to those in the a priori emissions inventory. From 2013 to 2016, we find a 44–88% reduction in the transport sector emissions (0.92–4.4×105 Mg in 2016) and a minimum 61% decrease in residential sector emissions (<3.5×105 Mg in 2016) within the study area. We also provide evidence that the night-time fraction of traffic sources in 2016 was higher than that in the 2013 emissions inventory. This study shows the applicability of PEEs and high-resolution observations in providing timely updates of emission estimates by source sector.

Stochastic analysis of oscillatory hydraulic tomography

This paper investigates the effectiveness of different oscillatory hydraulic tomography (OHT) frequencies for estimating heterogeneous fields. The analysis first formulates the effects of uncharacterized aquifer responses, T, and S fields as the ensemble mean residual flux and residual storage terms in the stochastic unconditional and conditional mean equations. These terms persist unless the T, S, or head fields are specified everywhere. We then conducted OHT to estimate the T and S fields using Monte Carlo experiments. The experiments show that using the heads in response to periodic pumping with different frequencies or multifrequency, the estimates' performance metrics vary from one realization to others. The mean performance metrics over many realizations are, however, indistinguishable, despite the frequency. We attribute the variation in the performance metrics to the lack of parameter or state variable ergodicity. Lastly, we emphasize the importance of dense monitoring networks and a cost-effective data collection strategy to improve the resolution of characterizing aquifer heterogeneity.

Field Evaluation of Low-Cost PM Sensors (Purple Air PA-II) Under Variable Urban Air Quality Conditions, in Greece

Recent advances in particle sensor technologies have led to an increased development and utilization of low-cost, compact, particulate matter (PM) monitors. These devices can be deployed in dense monitoring networks, enabling an improved characterization of the spatiotemporal variability in ambient levels and exposure. However, the reliability of their measurements is an important prerequisite, necessitating rigorous performance evaluation and calibration in comparison to reference-grade instrumentation. In this study, field evaluation of Purple Air PA-II devices (low-cost PM sensors) is performed in two urban environments and across three seasons in Greece, in comparison to different types of reference instruments. Measurements were conducted in Athens (the largest city in Greece with nearly four-million inhabitants) for five months spanning over the summer of 2019 and winter/spring of 2020 and in Ioannina, a medium-sized city in northwestern Greece (100,000 inhabitants) during winter/spring 2019–2020. The PM2.5 sensor output correlates strongly with reference measurements (R2 = 0.87 against a beta attenuation monitor and R2 = 0.98 against an optical reference-grade monitor). Deviations in the sensor-reference agreement are identified as mainly related to elevated coarse particle concentrations and high ambient relative humidity. Simple and multiple regression models are tested to compensate for these biases, drastically improving the sensor’s response. Large decreases in sensor error are observed after implementation of models, leading to mean absolute percentage errors of 0.18 and 0.12 for the Athens and Ioannina datasets, respectively. Overall, a quality-controlled and robustly evaluated low-cost network can be an integral component for air quality monitoring in a smart city. Case studies are presented along this line, where a network of PA-II devices is used to monitor the air quality deterioration during a peri-urban forest fire event affecting the area of Athens and during extreme wintertime smog events in Ioannina, related to wood burning for residential heating.

High resolution spatial mapping of element concentrations in PM10: A powerful tool for localization of emission sources

A very-low volume sampler of particulate matter (PM) on membrane filters, recently developed with the purpose of allowing spatially-resolved determination of PM and of its chemical components, was employed from December 2016 to February 2018 in a wide and dense monitoring network across Terni, an urban and industrial hot-spot of Central Italy (23 sampling sites, about 1 km between each other). Terni basin can be considered as an open air laboratory for studying the spatial distribution of PM, as it includes several spatially disaggregated sources. PM10 samples were chemically characterized for the water-soluble and insoluble fraction of 33 elements (Al, As, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cs, Cu, Fe, Ga, K, La, Li, Mg, Mn, Mo, Na, Nb, Ni, Pb, Rb, Sb, Sn, Sr, Ti, Tl, U, W, Zn, Zr). Spatial variability of the element concentrations across Terni was then mapped by using the ordinary kriging interpolation method. Spatial distribution of the analyzed elements successfully traced the various PM10 sources. In particular, the spatial mapping of Ba (water-soluble fraction), Bi, Cu, Sb, Sn and Zr (insoluble fraction) traced PM10 emission from the rail network and vehicular traffic, Ce, Cs, La, Li, Rb, Sr and U (insoluble fraction) traced soil resuspension, Cd, Cs, K, Rb and Tl (water-soluble fraction) biomass burning and Co, Cr, Mn, Nb, Ni, Pb (insoluble fraction), As, Cr, Ga, Li, Mo, Mn, W and Zn (water-soluble fraction) the steel industry pole. Principal component analysis was performed on the spatially-resolved chemical data to cluster the elements tracing the main PM10 sources. The winter and summer size distribution of the water-soluble and insoluble elements was analyzed to verify their link with the emission sources. The proposed experimental approach promises to be very effective for the assessment of population exposure to different PM10 sources.

Development of a tailored combination of machine learning approaches to model volumetric soil water content within a mesic forest in the Pacific Northwest

To monitor the complex interdependency between soil moisture and productivity of Douglas-fir forests in the Pacific Northwest a dense low-cost network of soil moisture sensors was installed in association with 33 forest vegetation plots in a single 2260-ha watershed in western Oregon. Four sensors were installed in each plot to record volumetric soil water content and soil temperature 5 and 50 cm below the surface of mineral soil at 2-hr intervals from 2012 through 2018.Significant data gaps restricted the full utilization of the resulting dataset summarized as daily averages. A Self-Organizing Map neural network was applied to find clusters of plots with similar functional dependencies of soil temperature and soil moisture values. The results guided the subsequent assignment of the most suitable time series as input for plot-specific Distributed Time Delay Neural Networks that were trained to simulate the time series of volumetric water content in the two soil depths at 33 locations. In addition to contemporaneous inputs used in conventional neural networks, the application of Distributed Time Delay Neural Networks allows the incorporation of prior sections of the input time series data. The results show the high performance of the combined machine learning approaches that capitalize on the spatial data richness of the dense monitoring network. The correlation coefficient for the test data subsets averages 0.95 and the mean error equals 0.04 cm3/cm3. The correlation between time series at various plots was retained for the modeled data. A cross-comparison of the data revealed that 81% of the 5-cm soil moisture data and 95% of the 50-cm soil moisture data show less than 10% difference between the coefficients of determination prior to and after the gap-filling procedure. The long-term gap-filled soil moisture extremes correspond well with a high-resolution Palmer Drought Severity Index calculated for the area.

Spatial distribution of levoglucosan and alternative biomass burning tracers in atmospheric aerosols, in an urban and industrial hot-spot of Central Italy

Domestic biomass heating and wildfires strongly affect particulate matter (PM) concentration in the atmosphere. The individuation of alternative chemical tracers may provide a valuable tool to apportion different possible contributions to biomass burning. In this study, we used a new experimental procedure, based on high spatial resolution analyses of PM10, to assess the spatial distribution of levoglucosan (LVG) and evaluate the possible use of alternative biomass burning tracers in the Terni basin, a wide urban and industrial hot-spot of Central Italy, which includes several spatially disaggregated sources. Spatially-resolved chemical characterization of PM10 was obtained through the use of innovative samplers working in parallel in a dense monitoring network (20 sampling sites, about 1 km between each other), during wintertime. PM10 samples were analyzed for LVG, water-soluble organic carbon (WSOC) and water-soluble and insoluble fraction of 33 elements. Principal component analysis (PCA) on the obtained spatially-resolved data allowed us to identify biomass burning tracers across the polluted study area. Analyses of size-segregated PM samples showed the presence of LVG, and water-soluble Cd, Cs, K, Rb and Tl in particles with size fraction smaller than 1 μm, confirming them as tracers of combustion processes (mainly related to biomass burning). Pearson correlation coefficients demonstrated that concentrations of WSOC and water-soluble Cd, Cs, K, Rb and Tl were well correlated with the spatial variability of LVG concentration. The combined use of spatially and dimensionally resolved data was found to be particularly advantageous for the identification of alternative source tracers, which were used to reliably trace the main local biomass burning sources in the study area.

Case-study of neighborhood-scale summertime urban air temperature for the City of Vienna using crowd-sourced data

For the investigation of intra-urban air temperature patterns, high spatial resolution observations are needed. To reach such a dense monitoring network in a cost-efficient way, recently there have been efforts to utilize networks of citizen weather stations (CWS). Here, quality-controlled crowd-sourced CWS air temperature observations were used to study the temperature distribution in Vienna, Austrian, for an 11-day high-temperature period in August 2018. The statistical quality control was based on the procedure developed in a previous study, further optimized by incorporating two additional steps addressing the specific issue of radiative errors using solar radiation data from non-CWS reference stations. After application of the quality control, CWS air temperature data provide results comparable to stations of the national meteorological service network, and discrepancies between these data sets could be explained by differences in station location relative to the urban structure. In Vienna, 1357 unique stations were available during the study period, of which 1083 stations passed the full quality control procedure. Compared to the currently available operational network, this represents a major improvement in the spatial resolution of available temperature data. Hence, quality-controlled CWS data for Vienna seem a promising data source for further urban climate studies of the area.