Single Monitoring Station Research Articles

Electromagnetic exposure of population in three cities of Romania at four years difference, based on data recorded by the fixed national radiofrequency monitoring network

Abstract The blooming of wireless technologies and the deployment of the fifth generation (5G) of mobile communications have been accompanied by a significant increase in the risk perception related to human exposure to radio-frequency electromagnetic field (RF-EMF). Therefore, EMF monitoring has become necessary in areas with increased densification of the EMF sources. Present work made use of the raw data kindly provided by the National Authority for Management and Regulation in Communications (ANCOM) in Romania from the fixed EMF sensor network nationally deployed. We aimed to analyze the case of electric field level evolution after four years, in three cities. In each city a single monitoring station was chosen in the analysis. Database in the wide- and narrow-bandwidths covering the range (100 kHz – 7 GHz) was processed for whole years 2019 and 2023. Comparisons between locations and years were enabled by means of: a) descriptive statistics; b) time series analysis with Poincare plots for time-variability characterization; c) recurrence quantification analysis to emphasize the nonlinear dynamics of the exposure levels, respectively the recurring exposure levels dynamics. Similarities and dissimilarities were observed and discussed.

Novel spatio-temporal attention causal convolutional neural network for multi-site PM2.5 prediction

Multi-site PM2.5 prediction has emerged as a crucial approach, given that the accuracy of prediction models based solely on data from a single monitoring station may be constrained. However, existing multi-site PM2.5 prediction methods predominantly rely on recurrent networks for extracting temporal dependencies and overlook the domain knowledge related to air quality pollutant dispersion. This study aims to explore whether a superior prediction architecture exists that not only approximates the prediction performance of recurrent networks through feedforward networks but also integrates domain knowledge of PM2.5. Consequently, we propose a novel spatio-temporal attention causal convolutional neural network (Causal-STAN) architecture for predicting PM2.5 concentrations at multiple sites in the Yangtze River Delta region of China. Causal-STAN comprises two components: a multi-site spatio-temporal feature integration module, which identifies temporal local correlation trends and spatial correlations in the spatio-temporal data, and extracts inter-site PM2.5 concentrations from the directional residual block to delineate directional features of PM2.5 concentration dispersion between sites; and a temporal causal attention convolutional network that captures the internal correlation information and long-term dependencies in the time series. Causal-STAN was evaluated using one-year data from 247 sites in mainland China. Compared to six state-of-the-art baseline models, Causal-STAN achieves optimal performance in 6-hour future predictions, surpassing the recurrent network model and reducing the prediction error by 8%–10%.

Fine-grained PM[formula omitted] prediction in Lanzhou based on the spatiotemporal graph convolutional network

Urban fine-scale PM2.5 concentrations can be predicted that considers both spatial and temporal correlation, owing to the widespread installation of air quality micro-monitoring stations in cities. The traditional method only uses the data of a single monitoring station while ignoring the spatial diffusion characteristics of the influence between stations. Recently, urban PM2.5 predictions have often been insufficiently refined due to coarse-grained air quality data and poor consideration of spatial dependence in modelling. Here, the influence relationship of PM2.5 flow between irregular monitoring stations is abstracted into an unstructured correlation graph, combined with the PMPre-GCN model to forecast the PM2.5 concentration of the city’s intensive monitoring stations. The validity of this method was verified by using air quality monitoring data from 456 dense micro-stations in Lanzhou City as an example. The results illustrate that: (1) The construction of the correlation graph significantly improved the model’s prediction accuracy. Compared with the fully connected and distance-based graphs, the prediction results’ root means square error (RMSE) was reduced by 10.09% and 8.42%, respectively. (2) The density of monitoring sites makes the precision of fine-grained prediction higher. Using data from 456 stations resulted in a 36.31% reduction in RMSE compared to the selection of data from 228 stations to make inferences about air quality in the city. (3) PMPre-GCN showed the best prediction performance when predicting PM2.5 concentration from T+1 to T+8 h at all stations. Especially at T+8 h, RMSE and the mean absolute error are 11.83 μg/m3 and 8.21 μg/m3. Overall, the method is capable of fine-grained predictions of urban PM2.5 concentrations, thereby supporting resident health management and fine-grained government decision-making on the urban air environment.

A forecasting framework on fusion of spatiotemporal features for multi-station PM2.5

Persistent PM2.5 pollution poses a serious threat to human health. Developing an accurate urban regional PM2.5 forecasting is of practical significance for environmental protection. However, previous studies have mostly focused on individual monitoring stations, neglecting the influence of neighboring stations, which limits forecasting accuracy. Additionally, the PM2.5 of a single monitoring station cannot reflect the overall situation of a region. Therefore, this paper develops a novel PM2.5 spatiotemporal forecasting framework that combines graph convolutional module, temporal convolutional module, linear module. It enables the forecasting of PM2.5 concentrations at multiple stations and multiple time steps in the future. Concretely, we utilize a mixed graph convolutional network to extract the spatial features of PM2.5. Then, an improved temporal convolutional network, the second-order residual temporal convolutional network, is developed to capture complex temporal features. Following the classical “linear and non-linear” modeling strategy, a linear module is added to the forecasting framework. Experiments on the real air pollution dataset from Beijing demonstrate that our framework outperforms the state-of-the-art baselines.

Association of exposure to air pollutants with gestational diabetes mellitus in Chiayi City, Taiwan.

We investigated the associations of exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) and several gaseous pollutants with risk of gestational diabetes mellitus (GDM) in Taiwan. We retrospectively identified pregnant women who underwent a two-step approach to screen for GDM between 2006 and 2014. Information on concentrations of air pollutants (including PM2.5, sulfur dioxide [SO2], nitrogen oxides [NOx], and ozone [O3]) were collected from a single fixed-site monitoring station. We conducted logistic regression analyses to determine the associations between exposure to air pollutants and risk of GDM. A total of 11210 women were analyzed, and 705 were diagnosed with GDM. Exposure to PM2.5 during the second trimester was associated with a nearly 50% higher risk of GDM (odds ratio [OR] 1.47, 95% CI 0.96 to 2.24, p=0.077). The associations were consistent in the two-pollutant model (PM2.5 + SO2 [OR 1.73, p=0.038], PM2.5 + NOx [OR 1.52, p=0.064], PM2.5 + O3 [OR 1.96, p=0.015]), and were more prominent in women with age <30 years and body mass index <25 kg/m2 (interaction p values <0.01). Exposure to PM2.5 was associated with risk of GDM, especially in women who were younger or had a normal body mass index.

A Comprehensive Evaluation of Gridded L-, C-, and X-Band Microwave Soil Moisture Product over the CZO in the Central Ganga Plains, India

Recent developments in passive microwave remote sensing have provided an effective tool for monitoring global soil moisture (SM) observations on a spatiotemporal basis, filling the gap of uneven in-situ measurement distribution. In this paper, four passive microwave SM products from three bands (L, C, and X) are evaluated using in-situ observations, over a dry–wet cycle agricultural (mostly paddy/wheat cycle crops) critical zone observatory (CZO) in the Central Ganga basin, India. The L-band and C/X-band information from Soil Moisture Active Passive (SMAP) Passive Enhanced Level 3 (SMAP-L3) and Advanced Microwave Scanning Radiometer 2 (AMSR2), respectively, was selected for the evaluation. The AMSR2 SM products used here were derived using the Land Parameter Retrieval Model (LPRM) algorithm. Spatially averaged observations from 20 in-situ distributed locations were initially calibrated with a single and continuous monitoring station to obtain long-term ground-based data. Furthermore, several statistical metrices along with the triple collocation (TC) error model were used to evaluate the overall accuracy and random error variance of the remote sensing products. The results indicated an overall superior performance of SMAP-L3 with a slight dry bias (−0.040 m3·m−3) and a correlation of 0.712 with in-situ observations. This also met the accuracy requirement (0.04 m3·m−3) during most seasons with a modest accuracy (0.059 m3·m−3) for the entire experimental period. Among the LPRM datasets, C1 and C2 products behaved similarly (R = 0.621) with a ubRMSE of 0.068 and 0.081, respectively. The X-band product showed a relatively poor performance compared to the other LPRM products. Seasonal performance analysis revealed a higher correlation for all the satellite SM products during monsoon season, indicating a strong seasonality of precipitation. The TC analysis indicated the lowest error variance (0.02 ± 0.003 m3·m−3) for the SMAP-L3. In the end, we introduced Spearman’s rank correlation to assess the dynamic response of SM observations to climatic and vegetation parameters.

A Deep Learning Approach Using Graph Neural Networks for Anomaly Detection in Air Quality Data Considering Spatiotemporal Correlations

The ambient air pollution problem has become more severe as the social economy develops. Abnormal event detection in air quality data can prevent property loss and protect human health. The majority of existing anomaly detection models in air quality data are based on a single variable or a single monitoring station, ignoring spatial correlation and multivariate features of regional air pollutant concentrations. To address the aforementioned issues, this paper proposes a new deep learning approach that combines spatial correlation and temporal correlation of air quality data to detect air quality anomalies. The essential idea of this approach is to use the correlation degree between node information to fuse the spatial correlational feature and temporal correlational feature of air quality data to construct spatiotemporal graph structure data, which are used for anomaly detection in air quality data. In this proposed approach, (1) the weighted adjacency matrix is established to characterize the spatial correlation, and the feature matrix is constructed to characterize the temporal correlation; (2) changes in node information are transformed into changes in edge weights using the correlation degree between node information, and spatiotemporal graph structure data are constructed to characterize the spatiotemporal correlation; and (3) an advanced deep learning model, Context augmented Graph Autoencoder (Con-GAE), is utilized to handle the above spatiotemporal graph structure data and detect abnormal air quality events. The efficiency of the approach is demonstrated by anomaly detection on synthetic test sets produced from real-world datasets.

Air pollution and hospitalization of patients with idiopathic pulmonary fibrosis in Beijing: a time-series study

BACKGROUND AND AIM: A few studies from high-income countries suggested that traffic-related air pollutants (e.g. nitrogen oxides, elemental carbon) were associated with morbidity and mortality from idiopathic pulmonary fibrosis (IPF), a severe respiratory disease but is relatively less studied in relation to air pollution. Air quality in Beijing has been progressively improving since the 2013 Clean Air Act. We compiled data five years before and after 2013, and ran time-series analyses for 2008-2012 and 2013-2017 separately to investigate acute effects of ambient air pollution on IPF hospitalization risk in Beijing. METHODS: Daily counts of IPF hospitalizations were obtained from Beijing Public Health Information Center for the period 2008-2017 while daily city-wide average concentrations of each air pollutant (PM2.5, NO2, Ozone, SO2) were obtained from a single monitoring station set up in the US Embassy for 2008-2012 (for PM2.5 only) and from 35 municipal monitoring stations for 2013-2017. The associations between daily IPF hospitalization and average concentration of each pollutant were analyzed with a generalized additive model (GAM) estimating Poisson distribution. RESULTS:Across Beijing, daily 24-hour mean PM2.5 concentration during 2008-2012 and 2013-2017 was 88.9 and 76.7 μg/m3 respectively. During 2008-2012, relative risk (RR) of IPF hospitalization at lag0 per interquartile range (IQR) increase (83 μg/m3) in PM2.5 was 1.062 (95%CI: 1.025-1.101). The RR was reduced at lag1, lag2 and lag3 but remained significantly positive. In contrast, during 2013-2017, the RR was 1.049 (95%CI: 1.024-1.074) at lag0, but no significant associations were seen for all other lags. No associations were seen for NO2 whilst significant associations were observed with both SO2 and cool-season ozone. CONCLUSIONS:Despite improvement in overall air quality, acute exposure to high-level air pollution is a risk factor for IPF hospitalization in Beijing. Air quality policy should be continuously enforced and carefully monitored to protect public health in the long-term. KEYWORDS: air pollution, respiratory disease, time-series, LMIC

Time-series analysis of daily ambient temperature and emergency department visits in five US cities with a comparison of exposure metrics derived from 1-km meteorology products

BackgroundAmbient temperature observations from single monitoring stations (usually located at the major international airport serving a city) are routinely used to estimate heat exposures in epidemiologic studies. This method of exposure assessment does not account for potential spatial variability in ambient temperature. In environmental health research, there is increasing interest in utilizing spatially-resolved exposure estimates to minimize exposure measurement error.MethodsWe conducted time-series analyses to investigate short-term associations between daily temperature metrics and emergency department (ED) visits for well-established heat-related morbidities in five US cities that represent different climatic regions: Atlanta, Los Angeles, Phoenix, Salt Lake City, and San Francisco. In addition to airport monitoring stations, we derived several exposure estimates for each city using a national meteorology data product (Daymet) available at 1 km spatial resolution.ResultsAcross cities, we found positive associations between same-day temperature (maximum or minimum) and ED visits for heat-sensitive outcomes, including acute renal injury and fluid and electrolyte imbalance. We also found that exposure assessment methods accounting for spatial variability in temperature and at-risk population size often resulted in stronger relative risk estimates compared to the use of observations at airports. This pattern was most apparent when examining daily minimum temperature and in cities where the major airport is located further away from the urban center.ConclusionEpidemiologic studies based on single monitoring stations may underestimate the effect of temperature on morbidity when the station is less representative of the exposure of the at-risk population.

Visual Processing and Analysis of Landslide Deformation Based on GNSS

For landslide surface monitoring, the Global Navigation Satellite System (GNSS) has been widely used in landslides due to its real-time, all-weather, high-precision, simple operation and a high degree of automation. However, these data are not intuitive and visual data will be more interesting for users without professional knowledge. At the same time, the conventional data representation method is in the form of curves or tables for three-dimensional data of landslide surface deformation collected by GNSS. To make the data more intuitive, clear and valuable, it is easier for people to understand the process of landslide deformation and finally realize the visualization of decision. Here we show that a polar coordinate system rather than a Cartesian coordinate system is adopted to visualize the horizontal data, which not only shows the horizontal deformation of the landslide, but also easily knows the direction of the landslide deformation. The vertical data is in the form of slices rather than curves, which not only shows the deformation of the landslide surface, but also shows the process of the vertical change of the landslide in terms of the time series. Single GNSS monitoring station is composed of a GNSS receiver, GNSS antenna with random, a solar power unit, and a network transmission module. The system can be powered by the solar energy system, which can realize 24-hour unmanned operation, 7 days a week. The system can receive satellite signals in real-time process and analyze deformation data, then it automatically broadcast early warning information. Our results demonstrate that it is a better choice that the thematic map of Geographic Information System (GIS) is a technical system for collecting, storing, managing, calculating, analyzing and displaying geographic data supported by computer hardware and software systems. Here we show that the multi-dimensional properties of deformation monitoring and multiple expressions of the attribute values are displayed synchronously in order to obtain more useful information from the visual graphics.

Intense L-Band Solar Radio Bursts Detection Based on GNSS Carrier-To-Noise Ratio Decrease over Multi-Satellite and Multi-Station.

Intense solar radio bursts (SRBs) can increase the energy noise and positioning error of the bandwidth of global navigation satellite system (GNSS). The study of the interference from intense L-band SRBs is of great importance to the steady operation of GNSS receivers. Based on the fact that intense L-band SRBs lead to a decrease in the carrier-to-noise ratio () of multiple GNSS satellites over a large area of the sunlit hemisphere, an intense L-band SRB detection method without the aid of a radio telescope is proposed. Firstly, the valley period of a single satellite at a single monitoring station is detected. Then, the detection of SRBs is achieved by calculating the intersection of multiple satellites and multiple stations. The experimental results indicate that the detection rates of GPS L2 and GLONASS G2 are better than those of GPS L1 L5, GLONASS G1, and Galileo E1 E5. The detection rate of SRBs can reach 80% with a flux density above 800 solar flux unit (SFU) at the L2 frequency of GPS. Overall, the detection rate is not affected by the satellite distribution relative to the Sun. The proposed detection method is low-cost and has a high detection rate and low false alarm rate. This method is a noteworthy reference for coping with interference in GNSS from intense L-band SRBs.

Complex Characterization of Fine Fraction and Source Contribution to PM2.5 Mass at an Urban Area in Central Europe

It is well documented that Southern Poland is one of the most polluted areas in Europe due to the highest airborne concentrations of particulate matter (PM). Concentrations of fine particles are especially high in winter. Apart from detailed number concentrations, it is essential to accurately identify and quantify specific particulate pollution sources. Only a few Polish research centers are involved in such experiments—among them is Krakow research group. For the most part, research focuses on collecting 24-h average samples from stationary PM samplers at ambient monitoring sites and quantifying the specific elements and chemical constituents in PM. This approach includes modeling methods that can use the variability in physical and chemical PM characteristics as an input dataset to identify possible sources of the particles. The objective of this paper is to provide research results based on data collected from June 2018 to May 2019 from a single monitoring station at a central urban site. Careful comparison of data obtained prior to a 2019 law prohibiting solid fuel burning in the city of Krakow with data (2019–2020) when a regulation went into effect should indicate progress by noting lower PM levels. This work has shown that the method applied and Krakow results might be of interest to the broader community in regions of high PM concentration.

Correlación entre diferentes contaminantes atmosféricos de la Ciudad de México y el Área Metropolitana

Se lleva a cabo un estudio de correlación estadística entre datos numéricos correspondientes a la concentración de las partículas suspendidas y a la concentración de gases contaminantes que son obtenidos de las mediciones registradas en la base de datos de la Red Automática de Monitoreo Atmosférico (RAMA) para la Ciudad de México y el Área Metropolitana. Se reporta una importante dependencia entre los pares de gases NO2-NOX, NO-NOX, CO-NO, CO-NOX y los pares de partículas PM2.5-PM10, PMCO-PM10 cuando el estudio se realiza tomando como referencia una sola estación de monitoreo. Por otra parte, se encuentra una estrecha relación entre el ozono (O3) de una estación con el de otra para diferentes pares de estaciones.

Assessing the Feasibility of Satellite‐Based Thresholds for Hydrologically Driven Landsliding

AbstractElevated soil moisture and heavy precipitation contribute to landslides worldwide. These environmental variables are now being resolved with satellites at spatiotemporal scales that could offer new perspectives on the development of landslide warning systems. However, the application of these data to hydrometeorological thresholds (which account for antecedent soil moisture and rainfall) first needs to be evaluated with respect to proven, direct measurement‐based thresholds that use rain gages and in situ soil moisture sensors. Here we compare ground‐based hydrologic data to overlapping satellite‐based data before, during, and after a recent season of widespread shallow landsliding in the San Francisco Bay Area (California, USA). We then explore how the remotely sensed information could be used to empirically define hypothetical thresholds for shallow landsliding. We find that the ground‐based thresholds developed with a single monitoring station show superior performance because the in situ soil saturation data better reflect the gravity‐dominated subsurface flow conditions that are characteristic of hillslopes during the rainy season. Although the satellite‐based thresholds can identify most of the landslide days, they include a greater number of false alarms due to overestimates of soil moisture between major storm events. To avoid the type of false alarms that are characteristic of our satellite‐based thresholds, further postprocessing of the near‐surface hydrologic response data should be integrated into satellite‐based model outputs to better reflect gravity‐dominated drainage. Our results encourage further deployment of ground stations in landslide‐prone terrain and cautious exploration of satellite‐based hydrometeorological thresholds where in situ networks are nonexistent.

Comparison of temperature-mortality associations estimated with different exposure metrics

Background:Studies of the short-term association between ambient temperature and mortality often use temperature observations from a single monitoring station, frequently located at the nearest airport, to represent the exposure of individuals living across large areas. Population-weighted temperature estimates constructed from gridded meteorological data may offer an opportunity to improve exposure assessment in locations where station observations do not fully capture the average exposure of the population of interest.Methods:We compared the association between daily mean temperature and mortality in each of 113 United States counties using (1) temperature observations from a single weather station and (2) population-weighted temperature estimates constructed from a gridded meteorological dataset. We used distributed lag nonlinear models to estimate the 21-day cumulative association between temperature and mortality in each county, 1987–2006, adjusting for seasonal and long-term trends, day of week, and holidays.Results:In the majority (73.4%) of counties, the relative risk of death on extremely hot days (99th percentile of weather station temperature) versus the minimum mortality temperature was larger when generated from the population-weighted estimates. In contrast, relative risks on extremely cold days (first percentile of weather station temperature) were often larger when generated from the weather station observations. In most counties, the difference in associations estimated from the two temperature metrics was small.Conclusions:In a large, multi-site analysis, temperature-mortality associations were largely similar when estimated from weather station observations versus population-weighted temperature estimates. However, spatially refined exposure data may be more appropriate for analyses seeking to elucidate local health effects.

Prediction of Air Quality Based on KNN-LSTM

Since most of the existing air quality index (AQI) predicting models focused on prediction of the time series data of a single target monitoring station, they failed to consider the correlation and mutual influence among the air quality monitoring station sites and the spatio-temporal characteristics of air quality. And this will lead to a certain one-sidedness during air quality prediction of a particular site. Aimed at this problem, a short-term air quality prediction model based on K-nearest neighbor (KNN) and Long Short-Term Memory (LSTM) was proposed. The model firstly used KNN algorithm to select the time and space-related monitoring stations, then the air quality index sequences of these stations were constructed into data sets, followed by training and testing processes in the LSTM model, and finally the model was verified with real data. It is suggested that the prediction accuracy of the hybrid prediction model constructed in this paper is acceptable in terms of the space-time correlation, and it could be an alternative for further application of air quality prediction.

PM2.5 metal constituent exposure and stillbirth risk in Harris County, Texas

There is limited evidence on the role of exposure to chemical constituents of fine particulate matter (PM2.5) in risk for stillbirth. Thus, we conducted a case-control study in Harris County, Texas between 2008 and 2013, utilizing a 6:1 ratio of controls to individually matched cases. We linked birth and fetal death records with PM2.5 data from a single air monitoring station and estimated maternal exposures to metal constituents of PM2.5 over the risk period (defined by the gestational length of each case). We applied conditional logistic regression and modeled air pollutant exposures as continuous variables. We observed null associations for most metal constituents (Al, Cr, Cu, Fe, Pb, Mn, Ni, Se and Ti). However, we observed a modest increase in risk for stillbirth (adjusted odds ratio (OR) = 1.11, 95% confidence interval (CI): 1.02, 1.20) associated with a 0.007 μg/m3 interquartile range (IQR) increase in exposure to zinc (Zn) PM2.5. This study adds to the scarce literature evaluating associations between PM2.5 metal constituents and stillbirth and points to the need for further research in this area.

Effect of intra-urban temperature variation on tree flowering phenology, airborne pollen, and measurement error in epidemiological studies of allergenic pollen

Temperature gradients in cities can cause inter-neighborhood differences in the timing of pollen release. However, most epidemiological studies examining allergenic pollen utilize daily measurements from a single pollen monitoring station with the implicit assumption that the measured time series of airborne pollen concentrations applies across the study areas, and that the temporal mismatch between concentrations at the counting station and elsewhere in the study area is negligible. This assumption is tested by quantifying temperature using satellite imagery, observing flowering times of oak (Quercus) and mulberry (Morus) trees at multiple sites, and collecting airborne pollen. Epidemiological studies of allergenic pollen are reviewed and temperatures within their study areas are quantified. In this one-year study, peak oak flowering time was well explained by average February nighttime temperature (R2 = 0.94), which varied by 6 °C across Detroit. This relationship was used to predict flowering phenology across the study region. Peak flowering ranged from April 20–May 13 and predicted a substantial portion of relative airborne oak pollen concentrations in Detroit (R2 = 0.46) and at the regional pollen monitoring station (R2 = 0.61). The regional pollen monitoring station was located in a cooler outlying area where peak flowering occurred around May 12 and peak pollen concentrations were measured on May 15. This provides evidence that the timing of pollen release varies substantially within a metropolitan area and challenges the assumption that pollen measurements at a single location are representative of an entire city. Across the epidemiological studies, 50% of study areas were not within 1 °C (equal to a lag or lead of 4 days in flowering time) of temperatures at the pollen measurement location. Epidemiological studies using a single pollen station as a proxy for pollen concentrations are prone to significant measurement error if the study area is climatically variable.

Measurement Error in Epidemiological Studies of Allergenic Pollen Due to Heterogeneity in Flowering Phenology

Most epidemiological studies of allergenic pollen compare daily measurements from a pollen monitoring station with health outcomes from across a city or region, implicitly assuming that pollen is released at the same time across the study areas. However, cities contain temperature gradients that could cause trees in some neighborhoods to flower substantially earlier or later, potentially resulting in differences in airborne pollen concentrations between the regional pollen monitoring station and neighborhoods within the study area. The resulting pollen exposure measurement errors could be large and may result in substantial bias. In this study, epidemiological studies of allergenic pollen are reviewed and temperature gradients within their study areas are quantified. In addition, the effects of an intra-urban temperature gradient on flowering time and airborne pollen concentrations were measured in an observational study conducted on oak and mulberry trees in Detroit, USA. In the epidemiological studies, 50% of study areas were not within 1&amp;#176; C of the pollen measurement location. In the field study, a difference of 1&amp;#176; C resulted in 4 and 10 days difference in flowering time for oak and mulberry. Airborne pollen concentrations were correlated with estimated flowering time for oak (R2 &amp;#61; 0.25, p &amp;lt; 0.001) and mulberry (R2 &amp;#61; 0.68, p &amp;lt; 0.001) but were only weakly correlated with pollen concentrations measured at the pollen monitoring station for oak (R2 &amp;#61; 0.07, p &amp;gt; 0.05) and mulberry (R2 &amp;#61; 0.04, p &amp;gt; 0.05). These results provide evidence of intra-municipal heterogeneity in tree flowering and differences in airborne pollen concentrations between neighborhoods within a metropolitan area and the regional pollen monitoring station. This cautions against using measurements from a single pollen monitoring station as a proxy for pollen exposures if the study area contains climatic variability.

The connection of pollen concentrations and crowd-sourced symptom data: new insights from daily and seasonal symptom load index data from 2013 to 2017 in Vienna

Background Online pollen diaries and mobile applications nowadays allow easy and fast documentation of pollen allergy symptoms. Such crowd-sourced symptom data provides insights into the development and the onset of a pollen allergy. Hitherto studies of the symptom load index (SLI) showed a discrepancy between the SLI and the total pollen amount of a season, but did not analyze the daily data.Methods The Patient’s Hayfever Diary (PHD) was used as data pool for symptom data. Symptom data of Vienna (Austria) was chosen as a large and local sample size within the study period of 2013 until 2017. The city was divided into three different areas based on equal population densities and different environmental factors. Correlation factors, regression lines, locally weighted smoothing (LOESS) curves and line plots were calculated to examine the data.Results Daily SLI and pollen concentration data correlates well and the progress of the SLI within a pollen season is mirrored by the pollen concentrations. The LOESS curves do not deviate much from the regression line and support the linearity of the symptom-pollen correlation on a daily basis. Seasonal SLI data does not follow the same pattern as the respective seasonal pollen indices. Results did not vary in the three areas within Vienna or when compared with the Eastern region of Austria showing no significant spatial variation of the SLI.Discussion Results indicate a linear relationship of the SLI and pollen concentrations/seasonal polllen index (SPIn) on a daily basis for both in general and throughout the season, but not on a seasonal basis. These findings clarify the frequent misinterpretation of the SLI as index that is tightly connected to pollen concentrations, but reflects as well the seasonal variation of the burden of pollen allergy sufferers.Conclusion More than just the seasonal pollen index has to be considered when the SLI of a selected pollen season has to be explained. Cross-reactivity to other pollen types, allergen content and air pollution could play a considerable role. The similar behavior of the SLI in Vienna and a whole region indicate the feasibility of a possible symptom forecast in future and justifies the use of a single pollen monitoring station within a city of the size of Vienna.