Modeling and prediction of dengue occurrences in Kolkata, India, based on climate factors.

Dengue is one of the most serious vector-borne infectious diseases in India, particularly in Kolkata and its neighbouring districts. Dengue viruses have infected several citizens of Kolkata since 2012 and it is amplifying every year. It has been derived from earlier studies that certain meteorological variables and climate change play a significant role in the spread and amplification of dengue infections in different parts of the globe. In this study, our primary objective is to identify the relative contribution of the putative drivers responsible for dengue occurrences in Kolkata and project dengue incidences with respect to the future climate change. The regression model was developed using maximum temperature, minimum temperature, relative humidity and rainfall as key meteorological factors on the basis of statistically significant cross-correlation coefficient values to predict dengue cases. Finally, climate variables from the Coordinated Regional Climate Downscaling Experiment (CORDEX) for South Asia region were input into the statistical model to project the occurrences of dengue infections under different climate scenarios such as Representative Concentration Pathways (RCP4.5 and RCP8.5). It has been estimated that from 2020 to 2100, dengue cases will be higher from September to November with more cases in RCP8.5 (872 cases per year) than RCP4.5 (531 cases per year). The present research further concludes that from December to February, RCP8.5 leads to suitable warmer weather conditions essential for the survival and multiplication of dengue pathogens resulting more than two times dengue cases in RCP8.5 than in RCP4.5. Furthermore, the results obtained will be useful in developing early warning systems and provide important evidence for dengue control policy-making and public health intervention.

Quantifying urban heat island intensity and its physical mechanism using WRF/UCM.

Reliable quantification of urban heat island intensity (UHII) is crucial for the evaluation of extreme heat waves and the related heat stress. As a powerful approach for the study of urban climate, numerical models can simulate urban heat island (UHI) in both high spatial and temporal resolutions. However, accurate quantification of UHII using modelling grid data is still a challenge at present, due to the different criterions for the selection of urban/rural grids. This study simulates the high-resolution UHI in the city of Berlin using the Weather Research and Forecasting Model coupled with Urban Canopy Module. A new method to quantify UHII, which is based on the fitted linear functions of simulated 2-m air temperature (T2m) using the impervious surface area in WRF grids (ISAWRF), was adopted and evaluated. The simulated T2m matches the observations well, with a correlation coefficient of 0.95 (P < 0.01) and RMSE of 1.76 °C. The study area shows a strong UHI at nighttime. The simulated nighttime T2m increases with the increase in the ISAWRF. The linear functions of simulated nighttime T2m against ISAWRF are well fitted. The UHII is calculated as the products of the slopes of fitted functions and the largest ISAWRF. The derived UHII shows U-shaped diurnal variations, with high values at nighttime. The difference of simulated surface temperature and sensible heat flux between the impervious surface and the vegetation surface jointly determines the derived UHII. The large difference of surface temperature and the small difference of sensible heat flux between the impervious and the vegetation surface generate the high UHII at nighttime and vice versa during the daytime. The method of ISAWRF-based function of T2m overcomes the problems of traditional methods in arbitrary selecting urban/rural grids. It can be used easily to quantify UHII and to do the comparison study of UHII between different cities.

Interaction between urban heat island and urban pollution island during summer in Berlin.

Urban Heat Island (UHI) and Urban Pollution Island (UPI) are two major problems of the urban environment and have become more serious with rapid urbanization. Since UHI and UPI can interact with each other, these two issues should be studied concurrently for a better urban environment. This study investigated the interaction between the UHI and UPI in Berlin, through a combined analysis of in-situ and remote sensing observations of aerosols and meteorological variables in June, July, and August from 2010 to 2017. The atmospheric UHI (AUHI), surface UHI (SUHI), atmospheric UPI (AUPI), and near-surface UPI (NSUPI) were analyzed. The SUHI and AUPI are represented by the remote sensing land surface temperature (LST) and aerosol optical depth (AOD), and the AUHI and NSUPI are represented by the in-situ air temperature and Particulate Matter (PM10) concentrations. The study area shows spatial consistency between SUHI and AUPI, with higher LST and AOD in the urban areas. UHI strengthens the turbulent dispersion of particles in the urban areas, decreasing the NSUPI. The NSUPI intensity shows a negative relationship with the AUHI intensity, especially at night with a correlation coefficient of -0.31. The increased aerosols in urban atmosphere reduce the incoming solar radiation and increase the atmospheric longwave radiation in the urban areas. The response of the surface to the change of absorbed radiation is strong at night and weak during the day. This study estimates that the SUHI intensity is enhanced by around 12% at clear night by the increased absorbed radiation in the urban areas using an attribution method. The goal of this paper is to strengthen the understanding of the interactive influence between UHI and UPI and provide a basis for designing mitigation strategies of UHI and UPI.

A new method to quantify surface urban heat island intensity.

Reliable quantification of urban heat island (UHI) can contribute to the effective evaluation of potential heat risk. Traditional methods for the quantification of UHI intensity (UHII) using pairs-measurements are sensitive to the choice of stations or grids. In order to get rid of the limitation of urban/rural divisions, this paper proposes a new approach to quantify surface UHII (SUHII) using the relationship between MODIS land surface temperature (LST) and impervious surface areas (ISA). Given the footprint of LST measurement, the ISA was regionalized to include the information of neighborhood pixels using a Kernel Density Estimation (KDE) method. Considering the footprint improves the LST-ISA relationship. The LST shows highly positive correlation with the KDE regionalized ISA (ISAKDE). The linear functions of LST are well fitted by the ISAKDE in both annual and daily scales for the city of Berlin. The slope of the linear function represents the increase in LST from the natural surface in rural regions to the impervious surface in urban regions, and is defined as SUHII in this study. The calculated SUHII show high values in summer and during the day than in winter and at night. The new method is also verified using finer resolution Landset data, and the results further prove its reliability.

Influence of meteorological parameters during the preceding fall and winter on the questing activity of nymphal Ixodes ricinus ticks.

Wood ticks, Ixodes ricinus L., serve as vectors for various pathogens and are ubiquitous throughout Central Europe. Survival and development of I. ricinus depend on biotic and abiotic factors. We examined whether relative humidity (RH), air (T a ) and soil temperatures (T s ), or snow depth during November through February affect the questing activity of ticks during their subsequent season of activity. We related the number of host-seeking nymphs to meteorological parameters measured in close proximity at minutely intervals over the period of 6 years (2010-2015) in an urban park in Berlin. We defined thresholds at which associations appeared strongest. Although the annual variations in RH, T a , and snow depth were typical of the mid-latitudes, the questing activity of nymphs during their first peak of activity (March through July) varied among the 6 years more than threefold. The accumulated hours of RH below 77% in 2 m height during November through February affected the questing activity of nymphs during the following activity peak. In contrast to T a , accumulated hours of T s below -1 °C in 0.02 m depth or below -4 °C in 0.05 m depth during the preceding period significantly influenced the average number of nymphs questing during spring. Our observations suggest that RH, T s , and snow cover during the preceding months affect the questing activity of nymphal I. ricinus during their first peak of activity. Snow cover serves as an insulator between the atmosphere and soil, which not only stabilizes T s but also appears to protect ticks from exposure to frost and frequent temperature shifts.

Re-construction of the shut-down PM10 monitoring stations for the reliable assessment of PM10 in Berlin using fuzzy modelling and data transformation.

A dense monitoring network is vital for the reliable assessment of PM10 in different parts of an urban area. In this study, a new idea is employed for the re-construction of the 20 shut-down PM10 monitoring stations of Berlin. It endeavours to find the non-linear relationship between the hourly PM10 concentration of both the still operating and the shut-down PM10 monitoring stations by using a fuzzy modelling technique, called modified active learning method (MALM). In addition, the simulations were performed by using not only raw PM10 databases but also log-transformed PM10 databases for skewness reduction. According to the results of hourly PM10 simulation (root mean square error about 13.0 µg/m3, correlation coefficient 0.88), the shut-down stations have been appropriately simulated and the idea of dense monitoring network development by the re-construction of the shut-down stations was realised. The results of simulations using raw and log-transformed databases showed that data transformation has no significant effect on the performance of MALM in the simulation of shut-down PM10 stations. By the combination of the 11 still operating stations and the 20 re-constructed stations, a dense monitoring network was generated for Berlin and was utilised for the calculation of the reliable monthly and mean annual PM10 concentration for five different PM10 zones in Berlin (the suburban-background, urban-background, urban-traffic, rural-background and suburban-traffic areas). The results showed that the mean annual concentration of PM10 at the five zones increased by about 13.0% in 2014 (26.3 µg/m3) in comparison with 2013 (23.3 µg/m3). Furthermore, the mean annual concentration of PM10 in the traffic lanes of the suburban (2013 25.0 µg/m3, 2014 26.9 µg/m3) and urban (2013 27.7 µg/m3, 2014 31.3 µg/m3) areas is about 14 and 20% higher than the PM10 concentration of suburban-background (2013 21.3 µg/m3, 2014 24.5 µg/m3) and urban-background (2013 23.0 µg/m3, 2014 26.1 µg/m3) areas, respectively.

Emergence of global scaling behaviour in the coupled Earth-atmosphere interaction.

Scale invariance property in the global geometry of Earth may lead to a coupled interactive behaviour between various components of the climate system. One of the most interesting correlations exists between spatial statistics of the global topography and the temperature on Earth. Here we show that the power-law behaviour observed in the Earth topography via different approaches, resembles a scaling law in the global spatial distribution of independent atmospheric parameters. We report on observation of scaling behaviour of such variables characterized by distinct universal exponents. More specifically, we find that the spatial power-law behaviour in the fluctuations of the near surface temperature over the lands on Earth, shares the same universal exponent as of the global Earth topography, indicative of the global persistent role of the static geometry of Earth to control the steady state of a dynamical atmospheric field. Such a universal feature can pave the way to the theoretical understanding of the chaotic nature of the atmosphere coupled to the Earth's global topography.

Weather conditions and their effect on the increase of the risk of type A acute aortic dissection onset in Berlin.

In this study, a minimum distance classification and forward feature selection technique are joined to determine the relationship between weather conditions and the increase of the risk of type A acute aortic dissection (AAD) events in Berlin. The results demonstrate that changes in the amount of cloudiness and air temperature are the most representative weather predictors among the studied parameters. A discrimination surface was developed for the prediction of AAD events 6 h ahead, and it is found that, under a specific amount of cloudiness and air temperature, the risk of AAD events in Berlin increases about 20 %.