Data-Driven Load Forecasting Using Machine Learning and Meteorological Data

Electrical load forecasting is very crucial for electrical power systems’planning and operation.Both electrical buildings’load demand and meteorological datasets may contain hidden patterns that are required to be investigated and studied to show their potential impact on load forecasting.The meteorological data are analyzed in this study through different data mining techniques aiming to predict the electrical load demand of a factory located in Riyadh,Saudi Arabia.The factory load and meteorological data used in this study are recorded hourly between 2016 and 2017.These data are provided by King Abdullah City for Atomic and Renewable Energy and Saudi Electricity Company at a site located in Riyadh.After applying the data pre-processing techniques to prepare the data,different machine learning algorithms,namely Artificial Neural Network and Support Vector Regression(SVR),are applied and compared to predict the factory load.In addition,for the sake of selecting the optimal set of features,13 different combinations of features are investigated in this study.The outcomes of this study emphasize selecting the optimal set of features as more features may add complexity to the learning process.Finally,the SVR algorithm with six features provides the most accurate prediction values to predict the factory load.

Spatial-temporal characteristics of drought detected from meteorological data with high resolution in Shaanxi Province, China

The spatial pattern of meteorological factors cannot be accurately simulated by using observations from meteorological stations(OMS) that are distributed sparsely in complex terrain. It is expected that the spatial-temporal characteristics of drought in regions with complex terrain can be better represented by meteorological data with the high spatial-temporal resolution and accuracy. In this study, Standard Precipitation Evapotranspiration Index(SPEI) calculated with meteorological factors extracted from ITPCAS(China Meteorological Forcing Dataset produced by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences) was applied to identify the spatial-temporal characteristics of drought in Shaanxi Province of China, during the period of 1979–2016. Drought areas detected by SPEI calculated with data from ITPCAS(SPEI-ITPCAS) on the seasonal scale were validated by historical drought records from the Chinese Meteorological Disaster Canon-Shaanxi, and compared with drought areas detected by SPEI calculated with data from OMS(SPEI-OMS). Drought intensity, trend and temporal ranges for mutations of SPEI-ITPCAS were analyzed by using the cumulative drought intensity(CDI) index and the Mann-Kendall test. The results indicated that drought areas detected from SPEI-ITPCAS were closer to the historical drought records than those detected from SPEI-OMS. Severe and exceptional drought events with SPEI-ITPCAS lower than –1.0 occurred most frequently in summer, followed by spring. There was a general drying trend in spring and summer in Shaanxi Province and a significant wetting trend in autumn and winter in northern Shaanxi Province. On seasonal and annual scales, the regional and temporal ranges for mutations of SPEI-ITPCAS were different and most mutations occurred before the year 1990 in most regions of Shaanxi Province. The results reflect the response of different regions of Shaanxi Province to climate change, which will help to manage regional water resources.

Towards Increasing Data Availability for Meteorological Services: Inter-Comparison of Meteorological Data from a Synoptic Weather Station and Two Automatic Weather Stations in Kenya

Meteorological data is useful for varied applications and sectors ranging from weather and climate forecasting, landscape planning to disaster management among others. However, the availability of these data requires a good network of manual meteorological stations and other support systems for its collection, recording, processing, archiving, communication and dissemination. In sub-Saharan Africa, such networks are limited due to low investment and capacity. To bridge this gap, the National Meteorological Services in Kenya and few others from African countries have moved to install a number of Automatic Weather Stations (AWSs) in the past decade including a few additions from private institutions and individuals. Although these AWSs have the potential to improve the existing observation network and the early warning systems in the region, the quality and capacity of the data collected from the stations are not well exploited. This is mainly due to low confidence, by data users, in electronically observed data. In this study, we set out to confirm that electronically observed data is of comparable quality to a human observer recorded data, and can thus be used to bridge data gaps at temporal and spatial scales. To assess this potential, we applied the simple Pearson correlation method and other statistical tests and approaches by conducting inter-comparison analysis of weather observations from the manual synoptic station and data from two Automatic Weather Stations (TAHMO and 3D-PAWS) co-located at KMD Headquarters to establish existing consistencies and variances in several weather parameters. Results show there is comparable consistency in most of the weather parameters between the three stations. Strong associations were noted between the TAHMO and manual station data for minimum (r = 0.65) and maximum temperatures (r = 0.86) and the maximum temperature between TAHMO and 3DPAWS (r = 0.56). Similar associations were indicated for surface pressure (r = 0.99) and RH (r > 0.6) with the weakest correlations occurring in wind direction and speed. The Shapiro test for normality assumption indicated that the distribution of several parameters compared between the 3 stations were normally distributed (p > 0.05). We conclude that these findings can be used as a basis for wider use of data sets from Automatic Weather Stations in Kenya and elsewhere. This can inform various applications in weather and climate related decisions.

Research on Spatial Statistical Downscaling Method of Meteorological Data Applied to Photovoltaic Prediction

Aiming at the low spatial resolution of meteorological data output from a numerical model in photovoltaic power prediction,a geographically weighted statistical downscaling method considers the influence factors such as normalized vegetation index(NDVI),digital elevation model(DEM),slope direction,longitude and latitude is proposed.This method is based on the correlation between meteorological data and NDVI,DEM,slope direction,latitude and longitude,and introduces DEM and local Moran index to improve the regression model,and obtains 100∗100 m high-resolution meteorological spatial distribution data.Finally,combining the measured data of the study area and the established EOF iterative downscaling method to verify and compare the downscaling results.The results show that the error between the downscaled meteorological data and the measured value is smaller,and the comprehensive downscaling accuracy of the geographically weighted regression method is higher,and the model fitting effect is better.Therefore,this method can effectively improve the influence of errors caused by lower resolution,and provide a more reliable meteorological basis for the prediction of photovoltaic power.

Application of Meteorological Data Operation System(MDOS)in Meteorological Operation

Meteorological data operation system(MDOS)is a comprehensive business platform that integrates data transmission and monitoring,quality control,processing,storage and distribution,and data services.The operation and implementation of MDOS has further improved the quality control system of meteorological data in Yunnan Province,adjusted the business layout of meteorological data,and made the availability ratio of meteorological data continue to increase.The availability ratio of meteorological data from national stations rose from 99.68% to 99.96%,and that of regional stations increased from 95.33% to 99.08% from 2016 to 2020.MDOS not only solves the problem of inconsistent data and quality control methods of various business platforms,but also provides guarantees for more refined and accurate weather forecasts and climate predictions.

Assessment of Meteorological Threats to the Coordinated Search and Rescue of Unmanned/Manned Aircraft

The architecture and working principle of coordinated search and rescue system of unmanned/manned aircraft,which is composed of manned/unmanned aircraft and manned aircraft,were first introduced,and they can cooperate with each other to complete a search and rescue task.Secondly,a threat assessment method based on meteorological data was proposed,and potential meteorological threats,such as storms and rainfall,can be predicted by collecting and analyzing meteorological data.Finally,an experiment was carried out to evaluate the performance of the proposed method in different scenarios.The experimental results show that the coordinated search and rescue system of unmanned/manned aircraft can be used to effectively assess meteorological threats and provide accurate search and rescue guidance.

PNN based crop disease recognition with leaf image features and meteorological data

An automatic crop disease recognition method was proposed in this paper,which combined the statistical features of leaf images and meteorological data.The images of infected crop leaves were taken under different environments of the growth periods,temperature and humidity.The methods of image morphological operation,contour extraction and region growing algorithm were adopted for leaf image enhancement and spot image segmentation.From each image of infected crop leaf,the statistical features of color,texture and shape were extracted by image processing,and the optimal meteorological features with the highest accuracy rate were obtained and selected by the attribute reduction algorithm.The fusion feature vector of the image was formed by combining the statistical features and the meteorological features.Then the probabilistic neural networks(PNNs)classifier was adopted to evaluate the classification accuracy.The experimental results on three cucumber diseased leaf image datasets,i.e.,downy mildew,blight and anthracnose,showed that the crop diseases can be effectively recognized by the integrated application of leaf image processing technology,the disease meteorological data and PNNs classifier,and the recognition accuracy rate was higher than 90%,which indicated that the PNNs classifier trained on the disease feature coefficients extracted from the crop disease leaves and meteorological data could achieve higher classification accuracy.

Evaluation of Water Losses by Evaporation in the Nakanbe Basin

A numerical approach to heat and mass transfer in a large water reservoir is presented. This water reservoir is likened to a parallelepiped reservoir whose vertical and lower walls are adiabatic and impermeable. The equations that govern natural convection in water are solved by the finite volume method and Thomas’salgorithm. The adequacy between the velocity and pressure fields is ensured by the SIMPLE algorithm. We are going to evaluate the water losses by evaporation from three dams in the Nakanbé basin in Burkina Faso for a period of thirty years, that is to say from January 1, 1991, to March 15, 2020. The three dams have a rate of evaporation greater than 40% of the volume of water stored. Indeed the rate of evaporation in each dam increases with the water filling rate in the reservoir: we have observed the following results for each dam in the Nakanbé basin;for the date of 02/27/1988 to 03/13/2020., the Loumbila dam received a total volume of stored water of 22.02 Mm3 and 10.57 Mm3 as the total volume of water evaporated at the same date. At the Ouaga dam (2 + 3), it stored a water volume of 4.06 Mm3 and evaporated 2.03 Mm3 of its storage volume from 01/01/1988 to 05/07/2016. Finally, with regard to the Bagré dam, it stored 745.16 Mm3 of water and 365.13 Mm3 as the volume of water evaporated from 01/01/1993 to 03/31/2020.

Effect of Meteorological Data Assimilation on Regional Air Quality Forecasts over the Korean Peninsula

The Weather Research and Forecasting model coupled with Chemistry(WRF-Chem),a type of online coupled chemistry-meteorology model(CCMM),considers the interaction between air quality and meteorology to improve air quality forecasting.Meteorological data assimilation(DA)can be used to reduce uncertainty in meteorological field,which is one factor causing prediction uncertainty in the CCMM.In this study,WRF-Chem and three-dimensional variational DA were used to examine the impact of meteorological DA on air quality and meteorological forecasts over the Korean Peninsula.The nesting model domains were configured over East Asia(outer domain)and the Korean Peninsula(inner domain).Three experiments were conducted by using different DA domains to determine the optimal model domain for the meteorological DA.When the meteorological DA was performed in the outer domain or both the outer and inner domains,the root-mean-square error(RMSE),bias of the predicted particulate matter(PM)concentrations,and the RMSE of predicted meteorological variables against the observations were smaller than those in the experiment where the meteorological DA was performed only in the inner domain.This indicates that the improvement of the synoptic meteorological fields by DA in the outer domain enhanced the meteorological initial and boundary conditions for the inner domain,subsequently improving air quality and meteorological predictions.Compared to the experiment without meteorological DA,the RMSE and bias of the meteorological and PM variables were smaller in the experiments with DA.The effect of meteorological DA on the improvement of PM predictions lasted for approximately 58-66 h,depending on the case.Therefore,the uncertainty reduction in the meteorological initial condition by the meteorological DA contributed to a reduction of the forecast errors of both meteorology and air quality.

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau:Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorological Forcing Data(PMFD)and the high spatial resolution and upscaled China Meteorological Forcing Data(CMFD)were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(SSiB4/TRIFFID)and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau(TP),a region with complex topography and sparse observations.By comparing the monthly Leaf Area Index(LAI)and Gross Primary Production(GPP)against observations,we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP,reducing RMSEs by 24.3%and 20.5%,respectively.The multi-year averaged GPP decreased from 364.68 gC m^(-2)yr^(-1)to 241.21 gC m^(-2)yr^(-1)with the percentage bias dropping from 50.2%to-1.7%.When using the high spatial resolution CMFD,the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5%and 9.5%,respectively.This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

Observed Impacts of Climate Variability on LULC in the Mesopotamia Region

Remote sensing analysis techniques have been investigated extensively,represented by a critical vision,and are used to advance our understanding of the impacts of climate change and variability on the environment.This study aims to find a means of analysis that relies on remote sensing techniques to demonstrate the effects of observed climate variability on Land Use and Land Cover(LULC)of the Mesopotamia region,defined as a historical region located in the Middle East.This study employed the combined analysis of the Normalized Difference Vegetation Index(NDVI),Land Surface Temperature(LST),and two statistical analysis methods(Pearson Correlation Analysis,r;Coefficient of Determination,R^(2)),which were applied using the Moderate Resolution Imaging Spectroradiometer data and observed surface meteorological data from 2000 to 2018.The resulting NDVI images show five LULC classes with NDVI values varying between−0.3 and 0.9.Furthermore,changes in the classified LULC area were compared statistically to those in NDVI values,where a positive relationship was found.Also,when the LST values and temperature are more extreme,the NDVI values were found to be smaller,suggesting a decrease in the density of vegetation cover.A negative correlation was found through Pearson correlation analysis(r=∼−0.64),indicating a direct effect of increased temperatures on LULC.Indeed,this negative relationship between NDVI and LST was proven using R^(2) values,where a two-dimensional scatter plot analysis showed that R^(2) ranges from 0.54 to 0.9.Ultimately,the results obtained from this study reveal changes that may have many prominent effects in the field of LULC classification,accelerating the implications of climate change and variability factors.

Prediction of the Diffuse Solar Energy on Horizontal at Different Selected Locations

The main objective of this paper is to predict the diffuse solar energy on a horizontal surface by using data of global solar energy (H) and diffuse solar energy (Hd) at different selected geographical locations in Saudi Arabia during the period time from 1980 to 2019. The low values of the root mean square error RMSE for all correlations indicated a good agreement between the measured and calculated values of Hd. The negative values of mean percentage error MPE % for all models show that for all locations, the proposed correlations slightly overestimate Hd, and the absolute values of MPE never reach 1.35%. The first, second and third order correlations between the diffuse solar fraction Hd/H and the clearness index Kt and between the diffuse transmittance Hd/H0 and the sunshine hours have been proposed for the selected locations using the method of regression analysis. The differences between the measured and calculated values of Hd show that a first order correlation between Hd/H and Kt can be used for estimating Hd at the present locations with good accuracy. However, second order correlations between Hd/H or Hd/H0 and S/So are recommended for estimating Hd at these locations. The average annual differences between measured and calculated values of diffuse solar energy Hd on horizontal at selected sites in the present research are discussed.

PV-Wind-Diesel System for Energy Supply on Remote Area Applied for Telecommunication Towers in Comoros

The Comoros is an archipelago in the Indian Ocean located in the Mozambique Channel between the African continent and Madagascar. Geographically Comoros is composed of four islands: Grande Comores, Anjouan, Mohéli and Mayotte (under French administration). Apart from Mayotte, the others three independent islands commonly known as the Union of the Comoros are suffering from energy stress since their independence year 1975 until nowadays. The energy supplied and distributed by the national electricity company, SONELEC produced by diesel engines, is not stable with so much load shedding all the time. This instability energy leads to a lack of a phone network in some telecommunications towers connected to grid. The majority of telecommunications towers are located in rural areas not connected to grid and running on diesel generators, which once again leads to a problem with the telecommunications network when diesel engines fail. The two competing companies which operate in the field of telecommunications in the Comoros, namely Comores Télécom, a national public company and Telma, the private one, are still unable to ensure the provision of the telecommunications network on a regular basis. This is why we propose in the present work, a sizing of hybrid system composed essentially of a diesel generator, a wind turbine and a photovoltaic solar system with storage in batteries for supplying telecommunications towers in order to permanently ensure the provision of the telecommunications network for the well-being of the population. Our future energy must be based on non-polluting energies with significant resources. Renewable energies are the best candidates but with intermittent production especially in rural areas not connected to the national electricity grid whose energy demand is more important to meet the needs of the population. The aim of this work is the sizing of a hybrid system composed of a diesel generator, a wind turbine and a photovoltaic solar system with storage in batteries for supplying telecommunications towers located in rural areas in the Comoros. In fact, to verify the performance of the hybrid system, a numerical study has been carried out with the HOMER 2.68 Beta software using meteorological data from the Comoros. The results obtained show that this hybrid combination is more profitable in the margin of economic cost and environmental assessments with a less expensive investment. These results also show a better optimization of Wind/PV/Battery of the hybrid system used, satisfying the demand and contributing to the preservation of the environment to fight against climate change with a low cost of energy.

Effects of Climate and Agricultural Practices on Temporal Progress of Sclerotinia sclerotiorum for Soybean in Brazil

Currently the soybean crop is affected by the white mold (Sclerotinia sclerotiorum). The disease can reduce the crop yield and quality and decrease the prices of agricultural lands. The aim of the current research was to assess epidemiology of white mold on soybean crop grown at Arapoti, PR, Brazil, utilizing data related to agricultural practices and local meteorological factors. The experiment was conducted in a naturally infested area. The soybean crop was sown on October 18th, 2011. The experimental design adopted herein was a randomized block in a factorial combination with 4 row spacings (0.35, 0.45, 0.60, 0.75 m) and 4 plant populations (150, 200, 250, 300 thousand plants per hectare) and 4 replications. The temporal analysis of the epidemic was evaluated using mathematical models, such as Logistics, Monomolecular and Gompertz, in order to determine the best model that described the progress of the disease as a function of local meteorological elements. For the incidence data it has been shown that both logistic and monomolecular models were those that were best fitted to the experimental data. For severity, the best model related to the experimental data was the logistic one. Either for incidence or for severity, air temperature was considered to be the environmental factor most affecting the progress of the disease. The variability in the apparent infection rates of white mold on soybean was not affected by different row spacings and plant populations;therefore, suggesting that macroclimatic variations prevailed in such a fashion to mitigate the effect of cultural practices adopted in the field.

Spatiotemporal Variations and Controls on Anthropogenic Heat Fluxes in 12 Selected Cities in the Eastern China

Spatiotemporal variations of anthropogenic heat flux(AHF)is reported to be associated with global warming.However,confined to the low spatial resolution of energy consumption statistical data,details of AHF was not well descripted.To obtain high spatial resolution data of AHF,Defense Meteorological Satellite Program/Operational Linescan System(DMSP/OLS)nighttime light time-series product and Moderate Resolution Imaging Spectroradiometer(MODIS)satellite monthly normalized difference vegetation index(NDVI)product were applied to construct the human settlement index.Based on the spatial regression relationship between human settlement index and energy consumption data.A 1-km resolution dataset of AHF of 12 selected cities in the eastern China was obtained.Ordinary least-squares(OLS)model was applied to detect the mechanism of spatial patterns of AHF.Results showed that industrial emission in selected cities of the eastern China was accountable for 63%of the total emission.AHF emission in megacities,such as Tianjin,Jinan,Qingdao,and Hangzhou,was most significant.AHF increasing speed in most areas in the chosen cities was quite low.High growth or extremely high growth of AHF were located in central downtown areas.In Beijing,Shanghai,Guangzhou,Jinan,Hangzhou,Changzhou,Zhaoqing,and Jiangmen,a single kernel of AHF was observed.Potential influencing factors showed that precipitation,temperature,elevation,normalized different vegetation index,gross domestic product,and urbanization level were positive with AHF.Overall,this investigation implied that urbanization level and economic development level might dominate the increasing of AHF and the spatial heterogeneousness of AHF.Higher urbanization level or economic development level resulted in high increasing speeds of AHF.These findings provide a novel way to reconstruct of AHF and scientific supports for energy management strategy development.

Numerical Modeling and Technico-Economic Analysis of a Hybrid Energy Production System for Self-Consumption: Case of Rural Area in the Comoros

This study aims to provide electricity to a remote village in the Union of Comoros that has been affected by energy problems for over 40 years. The study uses a 50 kW diesel generator, a 10 kW wind turbine, 1500 kW photovoltaic solar panels, a converter, and storage batteries as the proposed sources. The main objective of this study is to conduct a detailed analysis and optimization of a hybrid diesel and renewable energy system to meet the electricity demand of a remote area village of 800 to 1500 inhabitants located in the north of Ngazidja Island in Comoros. The study uses the Hybrid Optimization Model for Electric Renewable (HOMER) Pro to conduct simulations and optimize the analysis using meteorological data from Comoros. The results show that hybrid combination is more profitable in terms of margin on economic cost with a less expensive investment. With a diesel cost of $1/L, an average wind speed of 5.09 m/s and a solar irradiation value of 6.14 kWh/m2/day, the system works well with a proportion of renewable energy production of 99.44% with an emission quantity of 1311.407 kg/year. 99.2% of the production comes from renewable sources with an estimated energy surplus of 2,125,344 kWh/year with the cost of electricity (COE) estimated at $0.18/kWh, presenting a cost-effective alternative compared to current market rates. These results present better optimization of the used hybrid energy system, satisfying energy demand and reducing the environmental impact.

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

Impact of Cold Spells on Ischemic Stroke Severity and Clinical Outcomes in Tianjin, China

There are several studies on the impact of extreme weather on the incidence and mortality of stroke, but the research on the effect of extreme weather events on severity and outcome of ischemic stroke is scarce. This study aimed to evaluate the impact of cold spells on the severity and clinical outcomes of patients with initial acute ischemic stroke in a hospital-based study. We enrolled 553 patients with initial ischemic stroke during the cold seasons between 2016 and 2019. Patients were separated into the mild stroke group and moderate-to-severe stroke group according to the National Institutes of Health Stroke Scale(NIHSS) of the U.S., and good outcome group and poor outcome group according to modified Rankin Scale(mRS) scores. There are nine different definitions of cold spells according to intensities and duration time. After adjustment for climate factors, air quality index, and common risk factors, it is found that cold spells were associated with moderate-to-severe neurological deficits and clinical outcomes in overall ischemic stroke patients. Furthermore, the delay effect for stroke severity started at the beginning of the cold wave(Lag 0) and lasted up to 14 days(Lags 0–14). In addition, when a cold spell was set as a daily mean temperature < 3rd or 5th percentile and with a duration ≥ 2 or 3 consecutive days, cold spells had a significant impact on clinical outcomes, and there was a definite delay effect of at least 7 days(Lags 0–7) and it lasted up to 14days(Lags 0–14). At last, we concluded that cold spells may be a contributory factor for more severe neurological deficits and worse outcomes in patients with initial ischemic stroke.

Spatiotemporal distribution and predictors of tuberculosis incidence in Morocco

Background:Tuberculosis(TB)is a major health problem in Morocco.This study aims at examining trends in TB in Morocco and identifying TB spatial clusters and TB-associated predictors.Method:Country-level surveillance data was exploited.Kendall’s correlation test was used to examine trends and an exploratory spatial data analysis was conducted to assess the global and local patterns of spatial autocorrelation in TB rates(Moran’s I and local indicator of spatial association[LISA])at the prefecture/province level.Covariates including living in a prefecture versus living in a province,annual rainfall,annual mean temperature,population density,and AIDS incidence were controlled.An ordinary least squares regression was thus performed and both spatial dependence and heteroscedasticity were assessed.Results:A decrease in TB incidence rate was seen between 1995 and 2014(Kendall’s tau b=−0.72;P<0.0001).However,while the period between 2005 and 2014(10 last years)was considered,TB rate remained stable and as high as 84 per 100000 population per year(95%CI:83.7-84.3).The highest incidence rates were seen in Tanger-Assilah,Fez,Tetouen-M’diq Fnidaq,Inezgane Ait Melleoul,and Casablanca.From 2005 to 2014,while TB incidence rate was stable in Fez(P=0.500),Tetouen-M’diq Fnidaq(P=0.300),Casablanca(P=0.500),Mohammadia(P=0.146),Al Hoceima(P=0.364),and Guelmim(P=0.242),an increase in TB incidence rate was seen in Tanger-Assilah(Kendall’s tau=0.49;P=0.023)and a decrease in Salé(Kendall’s tau b=−0,54;P=0.014)and Inezgane-Ait Melloul(Kendall’s tau b=−0,67;P=0.0023).TB is strongly clustered in space(P-values of Moran’s I<0.01).Two distinct spatial regimes that affect TB spatial clustering were identified(east and west).In the east,both annual rainfall(P=0.003)and AIDS(P=0.0002)exert a statistically significant effect on TB rate.In the west,only the living area(prefecture versus province)was associated with TB rate(P=0.048).Conclusions:New information on TB incidence and TB-related predictors was provided to decision-making and to further pertinent research.Association between annual rainfall and TB may be of interest to be explored elsewhere.

Gaseous pollutant transport from an underground parking garage in a Mediterranean multi-story building—Effect of temporal resolution under varying weather conditions

Indoor air dynamics and quality in high density residential buildings can be complex as it is affected by both building parameters,pollution sources,and outdoor meteorological conditions.The present study used CONTAM simulations to investigate the intra-building transport and concentration of an inert pollutant continuously emitted from an underground garage of a 15-floor building under moderate Mediterranean weather.The effects of outdoor meteorological conditions(air temperature,wind speed and direction)on indoor distribution of the emitted pollutant was tested under constant conditions.The importance of using actual transient meteorological data and the impact of their temporal resolution on calculated concentrations and exposure levels were also investigated.Vertical profiles of air exchange rate(AER)and CO concentration were shown to be sensitive to indoor-outdoor temperature difference,which controls the extent of the stack effect and its importance relative to wind effect.Even under constant conditions,transient mode simulations revealed that the time needed for pollutant distribution to reach steady state can be quite long(>24h in some cases).The temporal resolution(Ih vs.8h)of the meteorological data input was also found to impact calculated exposure levels,in an extent that varied with time,meteorological conditions and apartment position.