TY  - JOUR
AB  - Ultrafine particles (UFP) are suspected to have a high toxic potential, but evidence from long-term epidemiological studies remains sparse since highly spatially resolved UFP data is lacking. We modelled long-term annual average total particle number concentration (PNC) as indicator for UFP for two middle-sized German cities (Augsburg and Regensburg) and their surroundings, which are part of the German National Cohort (NAKO), for subsequent linkage with health data. Supervised land use regression (LUR) models were developed for Augsburg, combining two previous measurement campaigns (monitoring sites: 2014/15: N = 20 and 2017: N = 6) and spatial predictors. To account for the time difference and repeated monitoring sites, we applied a generalized additive model (GAM) and a mixed model (MM). Models were internally validated using leave-one-out cross-validation (LOOCV). We transferred the models to the Regensburg region and externally validated our predictions using in-situ measurements carried out in 2020/21 at six monitoring sites. For both approaches, models showed highly adjusted explained variance and LOOCV R2 (GAM: 0.90 and 0.76; MM: 0.91 and 0.86). Similar predictors were selected, mainly indicators for road network and industrial areas. The external validation showed good agreement of measured and predicted PNC with Spearman correlation coefficient r = 0.75 (GAM) and 0.86 (MM), though both models tended to underestimate the concentrations. The two LUR models resulted in similar predictions and captured intra-city spatial patterns and city-rural gradients well. The Augsburg models could be effectively transferred to Regensburg since the study regions featured similar characteristics. To evaluate the predictive capability in novel study areas, external validation measurements are recommended.
AU  - Dallavalle, M.
AU  - Cyrys, J.
AU  - Sues, S.*
AU  - Kecorius, S.
AU  - Breitner-Busch, S.
AU  - Pickford, R.
AU  - Schneider, A.E.
AU  - Peters, A.
AU  - Wolf, K.
C1  - 75801
C2  - 58067
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Development and validation of land use regression models for ultrafine particles in Augsburg and Regensburg, Germany.
JO  - Urban Climate
VL  - 64
PB  - Elsevier
PY  - 2025
SN  - 2212-0955
ER  - 

TY  - JOUR
AB  - Air pollution poses a significant threat to global public health, particularly in sensitive areas like schools. Reliable air quality assessment is critical to inform effective policymaking and protect vulnerable populations. However, accurately assessing air quality in cities with limited monitoring networks remains a significant challenge. This study bridges this gap by integrating satellite-derived columnar data for nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO) with machine learning (ML) techniques—random forest (RF) and gradient boosting machine (GBM)—to develop spatial multi-pollutant classification maps for air quality assessment around schools in Vilnius, Lithuania, where ground-based monitoring stations are limited. The results showed that between the two ML models, RF demonstrated better performance than GBM, achieving an accuracy of 0.900, precision of 0.895, recall of 0.897, and an F1 score of 0.896. To enhance prediction accuracy, the models incorporate meteorological variables (e.g., temperature, wind speed, humidity) alongside urban characteristics (e.g., building density, road density, and proximity to road networks). Comparison with surface-level NO2, SO2, and CO concentrations reveals the model's capacity to capture pollution patterns, particularly in the city center and densely built urban areas. The findings indicate that 37 % of schools are situated within 100–250 m of major roads, where NO2 + CO and NO2 + CO + SO2 are the dominant columnar pollution classes. The results of the spatial analysis reveal that these schools are mostly in the city center primarily due to higher building and road densities, as well as lower levels of greenness.
AU  - Davtalab, M.*
AU  - Davulienė, L.*
AU  - Uogintė, I.*
AU  - Kecorius, S.
AU  - Lovrić, M.*
AU  - Byčenkiene, S.*
C1  - 75482
C2  - 58078
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Multi-pollutant air quality assessment around urban schools using machine learning.
JO  - Urban Climate
VL  - 62
PB  - Elsevier
PY  - 2025
SN  - 2212-0955
ER  - 

TY  - JOUR
AB  - Generating high-resolution spatial interpolations of temperature processes is a vital task for studying urban climate anomalies and their various consequences. Such processes often constitute a complex and demanding data environment: Anthropogenic and natural conditions of the urban landscape result in anisotropic spatial dependencies and trend patterns that often vary in diurnal and seasonal cycles. Two-step geostatistical methods such as residual kriging take spatial heterogeneity into account but ignore the temporal dimension, which can result in a significant loss of potentially useful information. In this study, we propose nonparametric spatial detrending to obtain a process that fulfills the assumptions of ordinary kriging. In our application to urban air temperature series from monitoring sites distributed over the urban and suburban area of Augsburg, Germany, we provide an in-depth analysis of time-varying spatial heterogeneity. By using sub-sampling to account for diurnal, seasonal, and spatial trends, we produce interpolation maps with a resolution of 100 m × 100 m. The validation in a narrower sense is based on cross-validation and shows favorable behavior of the proposed method even when sub-samples are neglected. The broader sense validation is based on hold-out monitoring sites and provides further empirical support for our proposal.
AU  - Wild, M.*
AU  - Behm, S.*
AU  - Beck, C.*
AU  - Cyrys, J.
AU  - Schneider, A.E.
AU  - Wolf, K.
AU  - Haupt, H.*
C1  - 64739
C2  - 52002
TI  - Mapping the time-varying spatial heterogeneity of temperature processes over the urban landscape of Augsburg, Germany.
JO  - Urban Climate
VL  - 43
PY  - 2022
SN  - 2212-0955
ER  - 

TY  - JOUR
AB  - Spatial and temporal variability of meteorological variables across urban areas due to differences in land surface characteristics is a common phenomenon. Most pronounced is the effect of land cover on air temperature. In this study, parametric and non-parametric statistical approaches (stepwise multiple linear regression, random forests) were applied in order to model sub-daily and daily spatial patterns of the urban heat island intensity in the major city of Augsburg, Southern Germany, and its rural surroundings. A large number of model setups utilizing variables from different land surface data sets as predictors and taking into account different seasonal, daily and meteorological situations was examined. The results were compared concerning different measures of model performance (mean squared skill score, mean squared error, explained variance). For individual setups and situations considerable skill with a mean squared skill score of up to 0.85 was reached. The best performing models were obtained from multiple linear regression for situations with low wind speeds and cloud cover in the morning and evening. Selected models were utilized to derive continuous spatial distributions of the air temperature deviations from a rural reference station. The resulting maps can be useful for various applications, e.g. in the context of urban planning.
AU  - Straub, A.*
AU  - Berger, K.*
AU  - Breitner-Busch, S.
AU  - Cyrys, J.
AU  - Geruschkat, U.
AU  - Jacobeit, J.*
AU  - Kühlbach, B.*
AU  - Kusch, T.
AU  - Philipp, A.*
AU  - Schneider, A.E.
AU  - Umminger, R.*
AU  - Wolf, K.
AU  - Beck, C.*
C1  - 57029
C2  - 47445
CY  - Radarweg 29, 1043 Nx Amsterdam, Netherlands
TI  - Statistical modelling of spatial patterns of the urban heat island intensity in the urban environment of Augsburg, Germany.
JO  - Urban Climate
VL  - 29
PB  - Elsevier
PY  - 2019
SN  - 2212-0955
ER  - 

TY  - JOUR
AB  - In this contribution air temperature differences among Local Climate Zone (LCZ) categories are analysed with special consideration of varying synoptic conditions. Analyses are based upon an LCZ mapping for the urban area of Augsburg (Bavaria, Southern Germany) and hourly air temperature data from a comprehensive logger network. Quality checked air temperature measurements have been stratified according to season, hour of the day and weather situation. For resulting subsamples thermal differences among LCZs have been determined and appropriate statistical tests have been applied. Results confirm that built up LCZs feature higher temperatures than natural LCZs and that most distinct differences among LCZs appear under undisturbed synoptic conditions. With increasing cloudiness and in particular with increasing wind speed differences among LCZs diminish. But, even for strongly disturbed synoptic conditions statistical significance of the influence of LCZs on thermal characteristics could be assured. Thus, our findings provide clear evidence that detectable thermal differences among LCZs are not restricted to „ideal “synoptic conditions but occur as well under disturbed conditions. However, to assure not only the statistical but also the climatological and in particular the bioclimatological and human health related relevance of the documented differences among LCZs further studies incorporating appropriate metrics are intended.
AU  - Beck, C.*
AU  - Straub, A.*
AU  - Breitner-Busch, S.
AU  - Cyrys, J.
AU  - Philipp, A.*
AU  - Rathmann, J.*
AU  - Schneider, A.E.
AU  - Wolf, K.
AU  - Jacobeit, J.*
C1  - 53920
C2  - 45148
SP  - 152-166
TI  - Air temperature characteristics of local climate zones in the Augsburg urban area (Bavaria, southern Germany) under varying synoptic conditions.
JO  - Urban Climate
VL  - 25
PY  - 2018
SN  - 2212-0955
ER  - 

TY  - JOUR
AB  - A continuous daily PM2.5 sampling campaign from 10 April till 8 June 2013, including three haze episodes, was conducted in Beijing. Chemical species, including EC, OC, water-soluble ions and inorganic elements, were analysed by a thermal/optical carbon analyser, IC and ICP-MS, respectively. A comparison of air quality during such haze episodes in relation to clear air situations, as well as the differences between the haze episodes was emphasised. The results showed that the most important fractions of PM2.5 during haze were SO4 2-, NO3 - and NH4 + (namely, SNA) which together accounted for 54-61% of the total PM2.5 mass. Estimated secondary organic carbon (SOC) was also found to be increased during haze, but the relative increase compared to clear days was much lower than for SNA, leading to a decrease in relative contribution of SOC to PM2.5 in the observed haze events. Cluster analyses from back trajectories showed four air mass clusters during spring 2013 and air flow, which was from the south-easterly directions, might favour the accumulation of PM2.5, especially SNA and anthropogenic elements. All these results proved that the anthropogenic air pollution in the Southeast of Beijing was responsible for the formation of hazes in Beijing during spring 2013.
AU  - Shen, R.*
AU  - Schäfer, K.*
AU  - Shao, L.*
AU  - Schnelle-Kreis, J.
AU  - Wang, Y.*
AU  - Li, F.
AU  - Liu, Z.*
AU  - Emeis, S.*
AU  - Schmid, H.P.*
C1  - 47749
C2  - 39569
TI  - Chemical characteristics of PM2.5 during haze episodes in spring 2013 in Beijing.
JO  - Urban Climate
PY  - 2015
SN  - 2212-0955
ER  -