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Data on evaluation of AQI for different season in Kerman, Iran, 2015.

ABSTRACT: The purpose of this data, was to evaluate the air quality index of Kerman city in different season of 2015. The data showed that the PM10 and O3 were highest in the winter season and PM2.5, CO, SO2 and NO2 in the spring season as the air quality indexes. The highest number of unhealthy days was observed in spring in relation to PM2.5 and PM10 pollutants. The data showed that 33 and 9 days of the spring season had unfavorable conditions in relation PM2.5 and PM10 pollutants respectively. Therefore, the pollutant responsible for air pollution in Kerman was PM2.5. By comparing the air quality index in different seasons of 2015 in terms of different pollutants, it was found that in most of the seasons, Kerman has a desirable air quality index.

SUBMITTER: Heidarinejad Z

PROVIDER: S-EPMC6171083 | biostudies-other | 2018 Oct

REPOSITORIES: biostudies-other

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Data on evaluation of AQI for different season in Kerman, Iran, 2015.

Heidarinejad Zoha Z Kavosi Ali A Mousapour Hassan H Daryabor Mohammad Reza MR Radfard Majid M Abdolshahi Anna A

Data in brief 20180912

The purpose of this data, was to evaluate the air quality index of Kerman city in different season of 2015. The data showed that the PM10 and O3 were highest in the winter season and PM2.5, CO, SO2 and NO2 in the spring season as the air quality indexes. The highest number of unhealthy days was observed in spring in relation to PM2.5 and PM10 pollutants. The data showed that 33 and 9 days of the spring season had unfavo ...[more]

PMID: 30294644

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Project description:BackgroundPlatyhelminth infections of livestock can result in considerable economic losses. This study aims to evaluate the spatial frequency of cystic echinococcosis (CE), dicrocoeliasis, and fascioliasis in livestock slaughtered in Iran during the years 2015-2019 and estimate direct costs associated with organ condemnation due to these parasites.MethodsAbattoir data from 413 abattoirs representing all 31 Iranian provinces were collected from the Iran Veterinary Organization. Infection prevalence was calculated per year at the province level. The Local Moran's I statistic was performed to evaluate spatial autocorrelation of animals positive at slaughter for the years 2015-2019. Direct costs associated with condemned livers were calculated for each parasitic condition, with costs associated with condemned lungs also included for CE.ResultsOverall prevalence values for the study timeframe were as follows: sheep and goat fascioliasis 1.56% (95% CI: 1.56-1.56%), cattle fascioliasis 3.86% (95% CI: 3.85-3.88%), sheep and goat dicrocoeliasis 4.63% (95% CI: 4.62-4.63%), cattle dicrocoeliasis 3.08% (95% CI: 3.07-3.09%), sheep and goat CE 5.32% (95% CI: 5.32-5.33%), and cattle CE 7.26% (95% CI: 7.24-7.28%). Northwest Iran had the highest prevalence of CE and fascioliasis. High infection areas for Dicrocoelium spp. included the provinces of Zanjan, Gilan, Qazvin, and Tehran, which are located in northern Iran. Direct economic losses for sheep and goat fascioliasis, dicrocoeliasis, and CE for the study period were US$13,842,759, US$41,771,377, and US$22,801,054, respectively. Direct economic losses for cattle fascioliasis, dicrocoeliasis, and CE for the study period were US$1,989,200, US$1,668,986, and US$2,656,568, respectively.ConclusionOur findings provide valuable data for future monitoring of these important parasitic diseases in Iranian livestock. Disease control strategies are required to reduce the economic and public health impact of these platyhelminths.

Project description:Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40?60 days and 35?45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

Project description:BACKGROUND:The A(H1N1)pdm09 virus strain used in the live attenuated influenza vaccine was changed for the 2015-2016 influenza season because of its lack of effectiveness in young children in 2013-2014. The Influenza Vaccine Effectiveness Network evaluated the effect of this change as part of its estimates of influenza vaccine effectiveness in 2015-2016. METHODS:We enrolled patients 6 months of age or older who presented with acute respiratory illness at ambulatory care clinics in geographically diverse U.S. sites. Using a test-negative design, we estimated vaccine effectiveness as (1-OR)×100, in which OR is the odds ratio for testing positive for influenza virus among vaccinated versus unvaccinated participants. Separate estimates were calculated for the inactivated vaccines and the live attenuated vaccine. RESULTS:Among 6879 eligible participants, 1309 (19%) tested positive for influenza virus, predominantly for A(H1N1)pdm09 (11%) and influenza B (7%). The effectiveness of the influenza vaccine against any influenza illness was 48% (95% confidence interval [CI], 41 to 55; P<0.001). Among children 2 to 17 years of age, the inactivated influenza vaccine was 60% effective (95% CI, 47 to 70; P<0.001), and the live attenuated vaccine was not observed to be effective (vaccine effectiveness, 5%; 95% CI, -47 to 39; P=0.80). Vaccine effectiveness against A(H1N1)pdm09 among children was 63% (95% CI, 45 to 75; P<0.001) for the inactivated vaccine, as compared with -19% (95% CI, -113 to 33; P=0.55) for the live attenuated vaccine. CONCLUSIONS:Influenza vaccines reduced the risk of influenza illness in 2015-2016. However, the live attenuated vaccine was found to be ineffective among children in a year with substantial inactivated vaccine effectiveness. Because the 2016-2017 A(H1N1)pdm09 strain used in the live attenuated vaccine was unchanged from 2015-2016, the Advisory Committee on Immunization Practices made an interim recommendation not to use the live attenuated influenza vaccine for the 2016-2017 influenza season. (Funded by the Centers for Disease Control and Prevention and the National Institutes of Health.).

Dataset Information

Data on evaluation of AQI for different season in Kerman, Iran, 2015.

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Data on evaluation of AQI for different season in Kerman, Iran, 2015.

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