Displaying publications 61 - 80 of 119 in total

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  1. Japanese encephalitis in Sarawak: studies on adult mosquito populations
    Hill MN
    Trans R Soc Trop Med Hyg, 1970;64(4):489-96.
    PMID: 4394984
    Matched MeSH terms: Weather
  2. Fulltext IoT-Based Sensor Data Fusion for Determining Optimality Degrees of Microclimate Parameters in Commercial Greenhouse Production of Tomato
    Rezvani SM, Abyaneh HZ, Shamshiri RR, Balasundram SK, Dworak V, Goodarzi M, et al.
    Sensors (Basel), 2020 Nov 12;20(22).
    PMID: 33198414 DOI: 10.3390/s20226474
    Optimum microclimate parameters, including air temperature (T), relative humidity (RH) and vapor pressure deficit (VPD) that are uniformly distributed inside greenhouse crop production systems are essential to prevent yield loss and fruit quality. The objective of this research was to determine the spatial and temporal variations in the microclimate data of a commercial greenhouse with tomato plants located in the mid-west of Iran. For this purpose, wireless sensor data fusion was incorporated with a membership function model called Optimality Degree (OptDeg) for real-time monitoring and dynamic assessment of T, RH and VPD in different light conditions and growth stages of tomato. This approach allows growers to have a simultaneous projection of raw data into a normalized index between 0 and 1. Custom-built hardware and software based on the concept of the Internet-of-Things, including Low-Power Wide-Area Network (LoRaWAN) transmitter nodes, a multi-channel LoRaWAN gateway and a web-based data monitoring dashboard were used for data collection, data processing and monitoring. The experimental approach consisted of the collection of meteorological data from the external environment by means of a weather station and via a grid of 20 wireless sensor nodes distributed in two horizontal planes at two different heights inside the greenhouse. Offline data processing for sensors calibration and model validation was carried in multiple MATLAB Simulink blocks. Preliminary results revealed a significant deviation of the microclimate parameters from optimal growth conditions for tomato cultivation due to the inaccurate timer-based heating and cooling control systems used in the greenhouse. The mean OptDeg of T, RH and VPD were 0.67, 0.94, 0.94 in January, 0.45, 0.36, 0.42 in June and 0.44, 0.0, 0.12 in July, respectively. An in-depth analysis of data revealed that averaged OptDeg values, as well as their spatial variations in the horizontal profile were closer to the plants' comfort zone in the cold season as compared with those in the warm season. This was attributed to the use of heating systems in the cold season and the lack of automated cooling devices in the warm season. This study confirmed the applicability of using IoT sensors for real-time model-based assessment of greenhouse microclimate on a commercial scale. The presented IoT sensor node and the Simulink model provide growers with a better insight into interpreting crop growth environment. The outcome of this research contributes to the improvement of closed-field cultivation of tomato by providing an integrated decision-making framework that explores microclimate variation at different growth stages in the production season.
    Matched MeSH terms: Weather
  3. Investigation of factors affecting the injury severity of single-vehicle rollover crashes: A random-effects generalized ordered probit model
    Anarkooli AJ, Hosseinpour M, Kardar A
    Accid Anal Prev, 2017 Sep;106:399-410.
    PMID: 28728062 DOI: 10.1016/j.aap.2017.07.008
    Rollover crashes are responsible for a notable number of serious injuries and fatalities; hence, they are of great concern to transportation officials and safety researchers. However, only few published studies have analyzed the factors associated with severity outcomes of rollover crashes. This research has two objectives. The first objective is to investigate the effects of various factors, of which some have been rarely reported in the existing studies, on the injury severities of single-vehicle (SV) rollover crashes based on six-year crash data collected on the Malaysian federal roads. A random-effects generalized ordered probit (REGOP) model is employed in this study to analyze injury severity patterns caused by rollover crashes. The second objective is to examine the performance of the proposed approach, REGOP, for modeling rollover injury severity outcomes. To this end, a mixed logit (MXL) model is also fitted in this study because of its popularity in injury severity modeling. Regarding the effects of the explanatory variables on the injury severity of rollover crashes, the results reveal that factors including dark without supplemental lighting, rainy weather condition, light truck vehicles (e.g., sport utility vehicles, vans), heavy vehicles (e.g., bus, truck), improper overtaking, vehicle age, traffic volume and composition, number of travel lanes, speed limit, undulating terrain, presence of central median, and unsafe roadside conditions are positively associated with more severe SV rollover crashes. On the other hand, unpaved shoulder width, area type, driver occupation, and number of access points are found as the significant variables decreasing the probability of being killed or severely injured (i.e., KSI) in rollover crashes. Land use and side friction are significant and positively associated only with slight injury category. These findings provide valuable insights into the causes and factors affecting the injury severity patterns of rollover crashes, and thus can help develop effective countermeasures to reduce the severity of rollover crashes. The model comparison results show that the REGOP model is found to outperform the MXL model in terms of goodness-of-fit measures, and also is significantly superior to other extensions of ordered probit models, including generalized ordered probit and random-effects ordered probit (REOP) models. As a result, this research introduces REGOP as a promising tool for future research focusing on crash injury severity.
    Matched MeSH terms: Weather
  4. Internal characteristics of loose solid source of debris flow in Zhouqu
    Shufang Fan
    Sains Malaysiana, 2017;46:2179-2186.
    In this paper, with debris flow in Zhouqu as the research object, combined with experiments such as cation exchange capacity (CEC), mineral chemical composition and water quality analysis, relation between water and salt in solid source forming debris flow was studied via soil column leaching test and soluble salt analysis, and internal characteristics of debris flow was accordingly showed. It was found that, the soil was loose, and the content of gravel and sand was high, and the content of fine particle was low. The soluble contents at the slope of the accumulation body were described as, collapsed accumulation body > landslide accumulation body, slope toe > slope top, gentle slope > steep slope, also related to length of the slope. The results indicated that accumulations released a large number of base ion after intense weathering, which migrated with water, concentrated and enriched at the slope toe. Saline soil with high salt content collapsed when encountering water and then formed mudflow, thus becoming the internal power to trigger and initiate debris flow to some extent.
    Matched MeSH terms: Weather
  5. Influence of growth stage and season on the antioxidant constituents of Cosmos caudatus
    Mediani A, Abas F, Ping TC, Khatib A, Lajis NH
    Plant Foods Hum Nutr, 2012 Dec;67(4):344-50.
    PMID: 23054393 DOI: 10.1007/s11130-012-0317-x
    The impact of tropical seasons (dry and wet) and growth stages (8, 10 and 12 weeks) of Cosmos caudatus on the antioxidant activity (AA), total phenolic content (TPC) as well as the level of bioactive compounds were evaluated using high performance liquid chromatography (HPLC). The plant morphology (plant height) also showed variation between the two seasons. Samples planted from June to August (during the dry season) exhibited a remarkably higher bioactivity and height than those planted from October to December (during the wet season). The samples that were harvested at eight weeks of age during the dry season showed the highest bioactivity with values of 26.04 g GAE/100 g and 22.1 μg/ml for TPC and IC₅₀, respectively. Identification of phytochemical constituents in the C. caudatus extract was carried out by liquid chromatography coupled with diode array detection and electrospray tandem mass (LC-DAD-ESIMS/MS) technique and the confirmation of constituents was achieved by comparison with literature data and/or co-chromatography with authentic standards. Six compounds were indentified including quercetin 3-O-rhamnoside, quercetin 3-O-glucoside, rutin, quercetin 3-O-arabinofuranoside, quercetin 3-O-galactoside and chlorogenic acid. Their concentrations showed significant variance among the 8, 10 and 12-week-old herbs during both seasons.
    Matched MeSH terms: Weather
  6. Influence of Weather on Hardness and Surface Roughness of Maxillofacial Elastomeric Materials
    Mousa MA
    J Contemp Dent Pract, 2020 Jun 01;21(6):678-682.
    PMID: 33025938
    AIMS: The purpose of this study was to assess the influence of hot and dry weather on the hardness and surface roughness of four different maxillofacial silicone elastomeric materials (MFSEM) including two room-temperature vulcanized (RTV) and two high-temperature vulcanized (HTV) materials.

    MATERIALS AND METHODS: Eighty test specimens were fabricated according to the manufacturer's instructions into rectangular test specimens. The hardness and surface roughness were tested, after 6 months of exposure to natural hot and dry weather. The hardness was measured through the International Rubber Hardness Degree (IRHD) scale using an automated hardness tester. The surface roughness was measured using a novel 3D optical noncontact technique using a combination of a light sectioning microscope and a computer vision system. Statistical Package for Social Sciences software SPSS/version 24 was used for analysis and a comparison between two independent variables was done using an independent t test, while more than two variables were analyzed, F test (ANOVA) to be used followed by a post hoc test to determine the level of significance between every two groups.

    RESULTS: The hot and dry weather statistically influenced the hardness and surface roughness of MFSEM. Cosmesil M-511 showed the least hardness in test groups while A-2000 showed the hardest material (p < 0.05). A-2000 showed significant changes from rough in case of nonweathered to become smoother in weather followed by A-2186 (p < 0.05). Cosmesil M-511 showed the roughest material.

    CONCLUSION: Cosmesil M-511 showed the least hard MFSEM after outdoor weathering while A-2000, the highest and least material showed hardness and surface roughness, respectively.

    CLINICAL IMPLICATION: A-2000 had a high IRHD scale hardness. This makes this material more suitable for the replacement of ear and nose defects. Cosmesil M-511 is soft and easily adaptable material that makes the material more appropriate for the replacement of small facial defect with undercut area to be easily inserted and removed. Whilst A-2000 is smoother and finer in test specimens after weathering, Cosmesil M-511 became rougher after weathering.

    Matched MeSH terms: Weather
  7. Fulltext Incidence, risk factors and clinical epidemiology of melioidosis: a complex socio-ecological emerging infectious disease in the Alor Setar region of Kedah, Malaysia
    Hassan MR, Pani SP, Peng NP, Voralu K, Vijayalakshmi N, Mehanderkar R, et al.
    BMC Infect Dis, 2010;10:302.
    PMID: 20964837 DOI: 10.1186/1471-2334-10-302
    Melioidosis, a severe and fatal infectious disease caused by Burkholderia pseudomallei, is believed to an emerging global threat. However, data on the natural history, risk factors, and geographic epidemiology of the disease are still limited.
    Matched MeSH terms: Weather
  8. Fulltext Impact assessment of climate change in Iran using LARS-WG Model
    Farzanmanesh, Raheleh, Ahmad Makmom Abdullah, Shakiba, Alireza, Jamil Amanollahi
    Pertanika Journal of Science & Technology, 2012;20(2):299-311.
    MyJurnal
    Iran is situated in a very diverse environmental area. The climate of the region is varied and influencedby different patterns. In order to best describe the expected climate change impacts for the region,climate change scenarios and climate variables must be developed on a regional, or even site-specific,scale. The weather generator is one of the valid downscaling methods. In the current study, LARSWG(a weather generator) and the outputs from ECHO-G for present climate, as well as future timeslice of 2010-2039 based on A1 scenario, were used to evaluate LARS-WG as a tool at 13 synopticstations located in the north and northeast parts of Iran. The results obtained in this study illustratethat LARS-WG has a reasonable capability of simulating the minimum and maximum temperaturesand precipitation. In addition, the results showed that the mean precipitation decreased in Semnan, thesouth of Khorasan and Golestan. Meanwhile, the mean temperature during 2010-2039 would increaseby 0.5°C, especially in the cold season.
    Matched MeSH terms: Weather
  9. Fulltext Household survey of container-breeding mosquitoes and climatic factors influencing the prevalence of Aedes aegypti (Diptera: Culicidae) in Makkah City, Saudi Arabia
    Aziz AT, Dieng H, Ahmad AH, Mahyoub JA, Turkistani AM, Mesed H, et al.
    Asian Pac J Trop Biomed, 2012 Nov;2(11):849-57.
    PMID: 23569860 DOI: 10.1016/S2221-1691(12)60242-1
    To investigate the prevalence of container breeding mosquitoes with emphasis on the seasonality and larval habitats of Aedes aegypti (Ae. aegypti) in Makkah City, adjoining an environmental monitoring and dengue incidence.
    Matched MeSH terms: Weather*
  10. Fulltext Habitat disturbance results in chronic stress and impaired health status in forest-dwelling paleotropical bats
    Seltmann A, Czirják GÁ, Courtiol A, Bernard H, Struebig MJ, Voigt CC
    Conserv Physiol, 2017;5(1):cox020.
    PMID: 28421138 DOI: 10.1093/conphys/cox020
    Anthropogenic habitat disturbance is a major threat to biodiversity worldwide. Yet, before population declines are detectable, individuals may suffer from chronic stress and impaired immunity in disturbed habitats, making them more susceptible to pathogens and adverse weather conditions. Here, we tested in a paleotropical forest with ongoing logging and fragmentation, whether habitat disturbance influences the body mass and immunity of bats. We measured and compared body mass, chronic stress (indicated by neutrophil to lymphocyte ratios) and the number of circulating immune cells between several bat species with different roost types living in recovering areas, actively logged forests, and fragmented forests in Sabah, Malaysia. In a cave-roosting species, chronic stress levels were higher in individuals from fragmented habitats compared with conspecifics from actively logged areas. Foliage-roosting species showed a reduced body mass and decrease in total white blood cell counts in actively logged areas and fragmented forests compared with conspecifics living in recovering habitats. Our study highlights that habitat disturbance may have species-specific effects on chronic stress and immunity in bats that are potentially related to the roost type. We identified foliage-roosting species as particularly sensitive to forest habitat deterioration. These species may face a heightened extinction risk in the near future if anthropogenic habitat alterations continue.
    Matched MeSH terms: Weather
  11. Forecasting peak asthma admissions in London: an application of quantile regression models
    Soyiri IN, Reidpath DD, Sarran C
    Int J Biometeorol, 2013 Jul;57(4):569-78.
    PMID: 22886344 DOI: 10.1007/s00484-012-0584-0
    Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.
    Matched MeSH terms: Weather
  12. Forecasting asthma-related hospital admissions in London using negative binomial models
    Soyiri IN, Reidpath DD, Sarran C
    Chron Respir Dis, 2013 May;10(2):85-94.
    PMID: 23620439 DOI: 10.1177/1479972313482847
    Health forecasting can improve health service provision and individual patient outcomes. Environmental factors are known to impact chronic respiratory conditions such as asthma, but little is known about the extent to which these factors can be used for forecasting. Using weather, air quality and hospital asthma admissions, in London (2005-2006), two related negative binomial models were developed and compared with a naive seasonal model. In the first approach, predictive forecasting models were fitted with 7-day averages of each potential predictor, and then a subsequent multivariable model is constructed. In the second strategy, an exhaustive search of the best fitting models between possible combinations of lags (0-14 days) of all the environmental effects on asthma admission was conducted. Three models were considered: a base model (seasonal effects), contrasted with a 7-day average model and a selected lags model (weather and air quality effects). Season is the best predictor of asthma admissions. The 7-day average and seasonal models were trivial to implement. The selected lags model was computationally intensive, but of no real value over much more easily implemented models. Seasonal factors can predict daily hospital asthma admissions in London, and there is a little evidence that additional weather and air quality information would add to forecast accuracy.
    Matched MeSH terms: Weather*
  13. Fulltext Forecasting HFMD Cases Using Weather Variables and Google Search Queries in Sabah, Malaysia
    Jayaraj VJ, Hoe VCW
    Int J Environ Res Public Health, 2022 Dec 15;19(24).
    PMID: 36554768 DOI: 10.3390/ijerph192416880
    HFMD is a viral-mediated infectious illness of increasing public health importance. This study aimed to develop a forecasting tool utilizing climatic predictors and internet search queries for informing preventive strategies in Sabah, Malaysia. HFMD case data from the Sabah State Health Department, climatic predictors from the Malaysia Meteorological Department, and Google search trends from the Google trends platform between the years 2010-2018 were utilized. Cross-correlations were estimated in building a seasonal auto-regressive moving average (SARIMA) model with external regressors, directed by measuring the model fit. The selected variables were then validated using test data utilizing validation metrics such as the mean average percentage error (MAPE). Google search trends evinced moderate positive correlations to the HFMD cases (r0-6weeks: 0.47-0.56), with temperature revealing weaker positive correlations (r0-3weeks: 0.17-0.22), with the association being most intense at 0-1 weeks. The SARIMA model, with regressors of mean temperature at lag 0 and Google search trends at lag 1, was the best-performing model. It provided the most stable predictions across the four-week period and produced the most accurate predictions two weeks in advance (RMSE = 18.77, MAPE = 0.242). Trajectorial forecasting oscillations of the model are stable up to four weeks in advance, with accuracy being the highest two weeks prior, suggesting its possible usefulness in outbreak preparedness.
    Matched MeSH terms: Weather*
  14. Feedforward backpropagation, Genetic Algorithm approaches for predicting reference evapotranspiration
    Shafika Sultan Abdullah, M.A. Malek, Namiq Sultan Abdullah, A. Mustapha
    Sains Malaysiana, 2015;44:1053-1059.
    Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET0) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine hours (Rn), relative humidity (Rh) and wind speed (U2), from the related meteorological station are used in the prediction of ET0 values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET0 and could be recommended for modeling of ET0 in arid and semiarid regions.
    Matched MeSH terms: Weather
  15. Fulltext Extreme precipitation patterns in the Asia-Pacific region and its correlation with El Niño-Southern Oscillation (ENSO)
    An D, Eggeling J, Zhang L, He H, Sapkota A, Wang YC, et al.
    Sci Rep, 2023 Jul 08;13(1):11068.
    PMID: 37422491 DOI: 10.1038/s41598-023-38317-0
    In the Asia-Pacific region (APR), extreme precipitation is one of the most critical climate stressors, affecting 60% of the population and adding pressure to governance, economic, environmental, and public health challenges. In this study, we analyzed extreme precipitation spatiotemporal trends in APR using 11 different indices and revealed the dominant factors governing precipitation amount by attributing its variability to precipitation frequency and intensity. We further investigated how these extreme precipitation indices are influenced by El Niño-Southern Oscillation (ENSO) at a seasonal scale. The analysis covered 465 ERA5 (the fifth-generation atmospheric reanalysis of the European Center for Medium-Range Weather Forecasts) study locations over eight countries and regions during 1990-2019. Results revealed a general decrease indicated by the extreme precipitation indices (e.g., the annual total amount of wet-day precipitation, average intensity of wet-day precipitation), particularly in central-eastern China, Bangladesh, eastern India, Peninsular Malaysia and Indonesia. We observed that the seasonal variability of the amount of wet-day precipitation in most locations in China and India are dominated by precipitation intensity in June-August (JJA), and by precipitation frequency in December-February (DJF). Locations in Malaysia and Indonesia are mostly dominated by precipitation intensity in March-May (MAM) and DJF. During ENSO positive phase, significant negative anomalies in seasonal precipitation indices (amount of wet-day precipitation, number of wet days and intensity of wet-day precipitation) were observed in Indonesia, while opposite results were observed for ENSO negative phase. These findings revealing patterns and drivers for extreme precipitation in APR may inform climate change adaptation and disaster risk reduction strategies in the study region.
    Matched MeSH terms: Weather
  16. Experimental Amelioration of Harsh Weather Speeds Growth and Development in a Tropical Montane Songbird
    Mitchell AE, Boersma J, Anthony A, Kitayama K, Martin TE
    Am Nat, 2020 10;196(4):E110-E118.
    PMID: 32970467 DOI: 10.1086/710151
    AbstractOrganisms living at high elevations generally grow and develop more slowly than those at lower elevations. Slow montane ontogeny is thought to be an evolved adaptation to harsh environments that improves juvenile quality via physiological trade-offs. However, slower montane ontogeny may also reflect proximate influences of harsh weather on parental care and offspring development. We experimentally heated and protected nests from rain to ameliorate harsh montane weather conditions for mountain blackeyes (Chlorocharis emiliae), a montane songbird living at approximately 3,200 m asl in Malaysian Borneo. This experiment was designed to test whether cold and wet montane conditions contribute to parental care and postnatal growth and development rates at high elevations. We found that parents increased provisioning and reduced time spent warming offspring, which grew faster and departed the nest earlier compared with offspring from unmanipulated nests. Earlier departure reduces time-dependent predation risk, benefitting parents and offspring. These plastic responses highlight the importance of proximate weather contributions to broad patterns of montane ontogeny and parental care.
    Matched MeSH terms: Weather*
  17. Fulltext Evaluation of factors influencing the groundwater chemistry in a small tropical island of Malaysia
    Kura NU, Ramli MF, Sulaiman WN, Ibrahim S, Aris AZ, Mustapha A
    Int J Environ Res Public Health, 2013 May;10(5):1861-81.
    PMID: 23648442 DOI: 10.3390/ijerph10051861
    Groundwater chemistry of small tropical islands is influenced by many factors, such as recharge, weathering and seawater intrusion, among others, which interact with each other in a very complex way. In this work, multivariate statistical analysis was used to evaluate the factors controlling the groundwater chemistry of Kapas Island (Malaysia). Principal component analysis (PCA) was applied to 17 hydrochemical parameters from 108 groundwater samples obtained from 18 sampling sites. PCA extracted four PCs, namely seawater intrusion, redox reaction, anthropogenic pollution and weather factors, which collectively were responsible for more than 87% of the total variance of the island's hydrochemistry. The cluster analysis indicated that three factors (weather, redox reaction and seawater intrusion) controlled the hydrochemistry of the area, and the variables were allocated to three groups based on similarity. A Piper diagram classified the island's water types into Ca-HCO3 water type, Na-HCO3 water type, Na-SO4-Cl water type and Na-Cl water type, indicating recharge, mixed, weathering and leached from sewage and seawater intrusion, respectively. This work will provide policy makers and land managers with knowledge of the precise water quality problems affecting the island and can also serve as a guide for hydrochemistry assessments of other islands that share similar characteristics with the island in question.
    Matched MeSH terms: Weather
  18. Evaluating the Performance of a Relative Humidity-Based Warning System for Sooty Blotch and Flyspeck in Iowa
    Rosli H, Mayfield DA, Batzer JC, Dixon PM, Zhang W, Gleason ML
    Plant Dis, 2017 Oct;101(10):1721-1728.
    PMID: 30676929 DOI: 10.1094/PDIS-02-17-0294-RE
    A warning system for the sooty blotch and flyspeck (SBFS) fungal disease complex of apple, developed originally for use in the southeastern United States, was modified to provide more reliable assessment of SBFS risk in Iowa. Modeling results based on previous research in Iowa and Wisconsin had suggested replacing leaf wetness duration with cumulative hours of relative humidity (RH) ≥97% as the weather input to the SBFS warning system. The purpose of the present study was to evaluate the performance of a RH-based SBFS warning system, and to assess the potential economic benefits for its use in Iowa. The warning system was evaluated in two separate sets of trials-trial 1 during 2010 and 2011, and trial 2 during 2013-2015-using action thresholds based on cumulative hours of RH ≥97% and ≥90%, respectively, in conjunction with two different fungicide regimes. The warning system was compared with a traditional calendar-based system that specified spraying at predetermined intervals of 10 to 14 days. In trial 1, use of the RH ≥97% threshold caused substantial differences between two RH sensors in recording number of hours exceeding the threshold. When both RH thresholds were compared for 2013-2015, on average, RH ≥90% resulted in a 53% reduction in variation of cumulative hours between two identical RH sensors placed adjacent to each other in an apple tree canopy. Although both the SBFS warning system and the calendar-based system resulted in equivalent control of SBFS, the warning system required fewer fungicide sprays than the calendar-based system, with an average of 3.8 sprays per season (min = 2; max = 5) vs. 6.4 sprays per season (min = 5; max = 8), respectively. The two fungicide regimes provided equivalent SBFS control when used in conjunction with the warning system. A partial budget analysis showed that using the SBFS warning system with a threshold of RH ≥90% was cost effective for orchard sizes of >1 ha. The revised warning system has potential to become a valuable decision support tool for Midwest apple growers because it reduces fungicide costs while protecting apples as effectively as a calendar-based spray schedule. The next step toward implementation of the SBFS warning system in the North Central U.S. should be multiyear field testing in commercial orchards throughout the region.
    Matched MeSH terms: Weather
  19. El Niño driven haze over the Southern Malaysian Peninsula and Borneo
    Khan MF, Hamid AH, Rahim HA, Maulud KNA, Latif MT, Nadzir MSM, et al.
    Sci Total Environ, 2020 Aug 15;730:139091.
    PMID: 32413602 DOI: 10.1016/j.scitotenv.2020.139091
    The Southeast Asian (SEA) region is no stranger to forest fires - the region has been suffering from severe air pollution (known locally as 'haze') as a result of these fires, for decades. The fires in SEA region are caused by a combination of natural (the El Niño weather pattern) and manmade (slash-and-burn and land clearing for plantations) factors. These fires cause the emissions of toxic aerosols and pollutants that can affect millions of people in the region. Thus, this study aims to identify the impact of the SEA haze on the Southern region of the Malaysian Peninsula and Borneo region of East Malaysia using the entire air quality observation data at surface level in 2015. Overall, the concentration of PM10 was about two-fold higher during the haze period compared to non-haze period. The concentrations of CO, flux of CO and flux of BC were aligned with PM10 during the entire observation period. The wind field and cluster of trajectory indicated that the Southern Malaysian Peninsula and Borneo were influenced mainly from the wildfires and the combustion of peat soil in the Indonesian Borneo. This study finds that wildfires from Borneo impacted the Southern Malaysian Borneo more seriously than that from Sumatra region.
    Matched MeSH terms: Weather
  20. El Niño Altered Gut Microbiota of Children: A New Insight on Weather-Gut Interactions and Protective Effects of Probiotic
    Lau ASY, Mitsuyama E, Odamaki T, Xiao JZ, Liong MT
    J Med Food, 2019 Mar;22(3):230-240.
    PMID: 30183458 DOI: 10.1089/jmf.2018.4276
    Changes in weather often trigger a myriad of negative impacts on the environment, which eventually affect human health. During the early months of 2016, Malaysia experienced El Niño, with an extremely dry season of almost zero rainfall. At the same time, an increase of more than twofold in fecal secretary immunoglobulin-A (SIgA) levels of healthy preschool children aged 2-6 years was observed, accompanied by an increase in phylum Bacteroidetes, predominantly attributed to genus Bacteroides and Odoribacter, which also positively correlated with fecal SIgA levels. Here, we present evidence to illustrate the detrimental effects of weather change on a microscopic "environment," the human gut ecosystem. We also discuss the protective effects of probiotic against dysbiosis as induced by weather change. The increase in Bacteroidetes was at an expense of decreased genus Faecalibacterium and Veillonella (phylum Firmicutes), whereas children consuming probiotic had a decrease in genus Collinsella, Atopobium, and Eggerthella (phylum Actinobacteria) instead.
    Matched MeSH terms: Weather
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