Forest ecosystems are crucial for preserving biodiversity and providing ecosystem services. The Ticino Park is a temperate mixed forest, one of the few natural ecosystems in northern Italy, which is facing increasing natural and anthropogenic disturbances exacerbated by climate change. Remote sensing is a cost-effective tool for the indirect estimation of forest status. However, it has typically relied on indirect proxies that often have coarse spatio-temporal resolution. This study investigates the possibility of deriving high temporal resolution time series of forest traits to identify drought-induced anomalies and relate them to differences in forest type and environmental factors. Sentinel-2 images from 2017 to 2022 were analysed, with 2022 being characterised by a severe drought. Leaf area index (LAI), canopy chlorophyll content (CCC), and canopy water content (CWC) were retrieved from Sentinel-2 time series through the S2 Toolbox and validated using measurements collected during an intensive field campaign in 2022. A positive and statistically significant correlation was found for all traits. The best results were obtained for LAI (R2 = 0.75, nRMSE = 11.49 %) and CCC (R2 = 0.82, nRMSE = 13.56 %), while slightly worse results were obtained for CWC (R2 = 0.64, nRMSE = 8.84 %). The accurate retrieval of LAI, CCC and CWC enabled the analysis of the temporal and spatial variations of the daily standardised anomalies (DSA). CCC reached the most negative DSA values, highlighting its higher sensitivity in detecting the effects of water shortage compared to CWC and LAI. The statistical analysis showed that DSA was linked to forest types. Pine and black cherry exhibited the highest stress response, while hygrophilic black alder and chestnut were the least impacted. These results highlight the species-specific responses to drought and the importance of incorporating species information in forest monitoring. The developed methodology provides a cost-effective approach for monitoring forest status and supporting effective management strategies.

Plasmid pBI143, a potential human-specific microbial source tracking marker, has previously been tested only in the USA. This study evaluated the performance of the pBI143 marker in comparison with BacHum, gyrB, and HF183 TaqMan using quantitative PCR on 65 fecal-source samples. Furthermore, it examined the reduction of pBI143 by wastewater treatment processes and compared it to the reduction of other human-specific markers. The analysis included 48 samples previously tested for BacHum, gyrB, and HF183 TaqMan, along with 17 additional samples, raw and secondary-treated sewage (n = 2 each) and deer fecal samples (n = 13). The validation results indicated that pBI143 is highly sensitive and specific for detecting human fecal contamination, with a sensitivity, specificity, and accuracy of 100 %, 88 %, and 94 %, respectively. Furthermore, pBI143 outperformed the other human-specific markers, which, despite having high sensitivity (97 %–100 %), exhibited much lower specificity (44 %–66 %). The presence of pBI143 was tested in river water samples from 21 sites in Yamanashi Prefecture and was detected in 95 % of the samples, indicating highly likely that these water samples are contaminated by human sources. During wastewater treatment, pBI143 showed significant reduction, with the highest log10 reduction value (LRV) (3.4 ± 0.4 log10) among the tested markers. Although the LRV of pBI143 was comparable to that of other human-specific markers, it showed a higher LRV than that of Escherichia coli quantified by culture (2.7 ± 0.3 log10) and qPCR (2.1 ± 0.6 log10). The variation may stem from differences between E. coli and Bacteroides. Bacteroides, being strictly anaerobic, has less regrowth potential compared to E. coli. Overall, plasmid pBI143 exhibited strong potential as a human-specific MST marker with high sensitivity, specificity, and higher concentrations in human fecal samples, though its cross-reactivity with pig feces necessitates further validation across different regions and species.

Owing to climate change, numerous regions around the world are expected to experience heightened occurrences of extreme events, including heat waves and intense precipitation. This will disproportionately impact the well-being of urban populations. The implementation of green roofs is actively considered as a viable climate adaptation strategy enhancing the resilience of cities. Green roofs have the potential to mitigate elevated temperatures within urban environments and play a crucial role in retaining stormwater. While many cities have included green roofs as a proposed funding tool in their climate adaptation plans, there is still a knowledge gap regarding best practice design and the long-term performance of green roofs as they are sensitive to meteorological conditions. We analyzed lysimeter measurements for different roof designs (a traditional gravel roof, an extensive green roof, and a wetland green roof) in terms of their cooling potential (evapotranspiration rates) and retention capacity for stormwater events over 31 months in a time-series framework, to evaluate the impact of the local climate variability and conclude about the best maintenance practices. While the wetland green roof generally showed the best summertime cooling and runoff reduction (total retention of 82.4 % compared to 63.4 % for the extensive green roof, and 29.9 % for the gravel roof), respectively, under optimal conditions, the extensive green roof GR was able to perform comparably. However, when the evapotranspiration of the extensive green roof decreased during persistent dry periods (daily sum of from 1.27 mm to 0.73 mm), their cooling potential was low for these days. This is critical as hot days occurred more frequently for these periods (37 hot days of 106 days during dry periods; 7 hot days of 127 days during wet periods). This indicates the urgent need for summertime irrigation.