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In this paper, we present an approach that combines data-driven and physical modelling for predicting the runoff occurrence and volume at catchment scale. With that aim, we first estimated the runoff volume from recorded storms aided by the Green-Ampt infiltration model. Then, we used machine learning algorithms, namely LightGBM (LGBM) and Deep Neural Network (DNN), to predict the outputs of the physical model fed on a set of atmospheric variables (relative humidity, temperature, atmospheric pressure, and wind velocity) collected before or immediately after the beginning of the storm. Results for a small urban catchment in Madrid show DNN performed better in predicting the runoff occurrence and volume. Moreover, enriching the input primary atmospheric variables with auxiliary variables (e.g., storm intensity data recorded during the first hour, or rain volume and intensity estimates obtained from auxiliary regression methods) largely increased the model performance. We show in this manuscript data-driven algorithms shaped by physical criteria can be successfully generated by allowing the data-driven algorithm learn from the output of physical models. It represents a novel approach for physics-informed data-driven algorithms shifting from common practices in hydrological modelling through machine learning. • Data-driven and physical modelling are merged to predict runoff. • Data-driven algorithm fed on weather variables before the storm starts. • Light GBM and Deep Neural Networks were coupled with Green-Ampt model. [ABSTRACT FROM AUTHOR]

A number of studies have shown that there is a time trend towards a reduction in the effects of heat on mortality. In the case of cold, however, there is practically no research of this type and so there is no clearly defined time trend of the impact of cold on mortality. Furthermore, no other specific studies have yet analysed the time trend of the impact of both thermal extremes by age group. We analysed data on daily mortality due to natural causes (ICD-10: A00-R99) in the city of Madrid across the period 2001–2009 and calculated the impact of extreme temperatures on mortality using Poisson regression models for specific age groups. The groups of age selected coinciding with the pre-existing age-groups analyzed in previous papers. For heat waves the groups of age used were: <10 years, 10–17 years, 18–44 years, 45–64 years, 65–74 years and over-75 years. For cold waves the groups of age used were: <1 year; 1–5 years, 6–17 years, 18–44 years, 45–64 years, 65–74 years and over-75 years. <1, 1–17, 18–44, 45–66, 65–74 and over-75 years. We controlled for confounding variables, such as air pollution, noise, influenza, pollen, pressure and relative humidity, trend of the series, as well as seasonalities and autoregressive components of the series. The results of these models were compared to those obtained for the same city during the period 1986–1997 and published in different studies. Our results show a lightly reduction in the effects of heat, especially in the over-45-year age group. In the case of cold, the behaviour pattern was the opposite, with an increase in its effect. Heat adaptation and socio-economic and public-health prevention and action measures may be behind this amelioration in the effects of heat, whereas the absence of such actions in respect of low temperatures may account for the increase in the effects of cold on mortality. From a public health point of view, the implementation of cold wave prevention plans covering all age groups is thus called for. [ABSTRACT FROM AUTHOR]

This paper analyses the probabilistic future behaviour of heat-waves (HWs) in the city of Madrid in the twenty-first century, using maximum daily temperatures from twenty-one climate circulation models under two representative concentration pathways (RCP 8.5 & RCP 4.5). HWs are modelled considering three factors: number per annum, duration and intensity, characterised by three stochastic processes: Poisson, Gamma and truncated Gaussian, respectively. Potential correlations between these processes are also considered. The probabilistic temperature behaviour is combined with an epidemiological model with stochastic mortality risk following a generalized extreme value distribution (gev). The objective of this study is to obtain probability distributions of mortality and risk measures such as the mean value of the 5% of worst cases in the 21st century, in particular from 2025 to 2100. Estimates from stochastic models for characterising HWs and epidemiological impacts on human health can vary from one climate model to another, so relying on a single climate model can be problematic. For this reason, the calculations are carried out for 21 models and the average of the results is obtained. A sensitivity adaptation analysis is also performed. Under RCP 8.5 for 2100 for Madrid city a mean excess of 3.6 °C over the 38 °C temperature threshold is expected as the average of all models, with an expected attributable mortality of 1614 people, but these figures may be substantially exceeded in some cases if the highest-risk cases occur. [ABSTRACT FROM AUTHOR]

While the COVID-19 pandemic is still in progress, being under the fifth COVID-19 wave in Madrid, over more than one year, Spain experienced a four wave pattern. The transmission of SARS-CoV-2 pathogens in Madrid metropolitan region was investigated from an urban context associated with seasonal variability of climate and air pollution drivers. Based on descriptive statistics and regression methods of in-situ and geospatial daily time series data, this study provides a comparative analysis between COVID-19 waves incidence and mortality cases in Madrid under different air quality and climate conditions. During analyzed period 1 January 2020–1 July 2021, for each of the four COVID-19 waves in Madrid were recorded anomalous anticyclonic synoptic meteorological patterns in the mid-troposphere and favorable stability conditions for COVID-19 disease fast spreading. As airborne microbial temporal pattern is most affected by seasonal changes, this paper found: 1) a significant negative correlation of air temperature, Planetary Boundary Layer height, and surface solar irradiance with daily new COVID-19 incidence and deaths; 2) a similar mutual seasonality with climate variables of the first and the fourth COVID-waves from spring seasons of 2020 and 2021 years. Such information may help the health decision makers and public plan for the future. • Climate factors and air quality can trigger SARS-CoV-2 viral infection transmission. • Synoptic atmospheric circulation patterns are related to COVID-19 waves start-up. • 1st and 4th COVID-19 waves have similar correlations with environmental variables. • COVID-19 and climate driving factors have mutual seasonality patterns in Madrid. • Early health strategies are essential to control the COVID-19 pandemic in Madrid. [ABSTRACT FROM AUTHOR]

In this paper we establish the attributable risk on respiratory and cardiovascular disorders related to traffic intensity in Madrid. In contrast to previous related studies, the proposed approach directly associates road traffic counts to patient emergency admission rates instead of using primary air pollutants. By applying Shapley values over gradient boosting machines, a first selection step is performed among all traffic observation points based on their influence on patient emergency admissions at Gregorio Marañon hospital. A subsequent quantification of the relative risk associated to traffic intensity of the selected point is calculated via ARIMA and log-linear Poisson regression models. The results obtained show that 13% of respiratory cases are related to traffic intensity while, in the case of cardiovascular disorders, the percentage increases to 39%. Image 1 • There is an association between traffic intensity and hospital admissions in Madrid. • 13% of hospital admissions due to respiratory causes are related to traffic intensity. • 39% of hospital admissions due to circulatory causes are related to traffic intensity. • The decrease in traffic in cities is related to lower numbers of hospital admissions. [ABSTRACT FROM AUTHOR]