Population, land use and economic exposure estimates for Europe at 100 m resolution from 1870 to 2020

Exacerbated anthropogenic water pollution under climate change and urbanization

Anthropogenic water pollution severely threatens human society worldwide, yet the water pollution induced by combined sewer overflow (CSO) remains unclear within climate change and urbanization. Hence, this study integrated the general circulation model (GCM) and shared socioeconomic pathway (SSP) projections with water quality modeling, to analyze spatiotemporal patterns and future trends of CSO-induced water pollution under changing environments. Results demonstrated that the given area (Dresden, Germany) encountered significant CSO-induced pollution, with 14,860 kg (95 % confidence interval, CI: 9,040-15,630 kg) of particulate matter (SS), organic compounds (COD, TN, TP), and pharmaceuticals (Carbamazepine, Gabapentin, Ciprofloxacin, Sulfamethoxazole) being discharged annually. Climate change and urbanization exacerbated the severity of CSO-induced pollution, causing the discharged pollutants to reach a maximum annual load of 34,900 kg (CI: 21,400-44,100 kg), with up to 82.19 % of organic compounds and 75.28 % of pharmaceuticals being discharged by the top 25 % of extreme CSOs. GIS-based spatial analysis indicated the regional heterogeneities of CSO-induced pollution, the high-frequency CSOs were predominantly located in highly-impervious areas, while the high-load discharges mainly occurred in densely-populated areas. Scenario analysis revealed stronger temporal variabilities of CSO-induced pollution in the future, with the seasonal anomalies of discharged loads ranging from -86.18 % to 76.89 %. In addition, pharmaceutical pollution exhibited significant uncertainties under changing environments, and the CI of discharged load expanded by up to 131.71 %. The methods and findings herein yielded further insights into water quality management in response to changing environments.

Vulnerability assessment of heat waves within a risk framework using artificial intelligence

Current efforts to adapt to climate change are not sufficient to reduce projected impacts. Vulnerability assessments are essential to allocate resources where they are needed most. However, current assessments that use principal component analysis suffer from multiple shortcomings and are hard to translate into concrete actions. To address these issues, this article proposes a novel data-driven vulnerability assessment within a risk framework. The framework is based on the definitions from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, but some definitions, such as sensitivity and adaptive capacity, are clarified. Heat waves that occurred between 2001 and 2018 in Quebec (Canada) are used to validate the framework. The studied impact is the daily mortality rates per cooling degree-days (CDD) region. A vulnerability map is produced to identify the distributions of summer mortality rates in aggregate dissemination areas within each CDD region. Socioeconomic and environmental variables are used to calculate impact and vulnerability. We compared abilities of AutoGluon (an AutoML framework), Gaussian process, and deep Gaussian process to model the impact and vulnerability. We offer advice on how to avoid common pitfalls with artificial intelligence and machine-learning algorithms. Gaussian process is a promising approach for supporting the proposed framework. SHAP values provide an explanation for the model results and are consistent with current knowledge of vulnerability. Recommendations are made to implement the proposed framework quantitatively or qualitatively.