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Dataset results
6 results for “Chemical quality”
Enhancing accuracy of air quality and temperature forecasts during paddy crop-residue burning season in Delhi via chemical data assimilation
<p>This paper examines the accuracy of Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) generated 72 h fine particulate matter (PM<sub>2.5</sub>) forecasts in Delhi during the crop residue burning season of Oct-Nov 2017 with respect to assimilation of the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals, persistent fire emission assumption, and aerosol-radiation interactions. The assimilation significantly pushes the model AOD and PM<sub>2.5</sub> towards the observations with the largest changes below 5 km altitude in the fire source regions (northeastern Pakistan, Punjab, and Haryana) as well as the receptor New Delhi. WRF-Chem forecast with MODIS AOD assimilation, aerosol-radiation feedback turned on, and real-time fire emissions reduce the mean bias by 88-195 µg/m<sup>3</sup> (70-86%) with the largest improvement during the peak air pollution episode of 6-13 November 2017. Aerosol-radiation feedback contributes ~21%, ~25%, and ~24% to reduction in mean bias of the first, second, and third day of PM<sub>2.5 </sub>forecast. Persistence fire emission assumption is found to work really well, as the accuracy of PM<sub>2.5</sub> forecasts driven by persistent fire emissions was only 6% lower compared to those driven by real fire emissions. Aerosol-radiation feedback extends the benefits of assimilating satellite AOD beyond PM<sub>2.5</sub> forecasts to surface temperature forecast with a reduction in the mean bias of 0.9<sup>o</sup>C - 1.5<sup>o</sup>C (17-30%). These results demonstrate that air quality forecasting can benefit substantially from satellite AOD observations particularly in developing countries that lack resources to rapidly build dense air quality monitoring networks.</p>
Dataset for "Quality improvement of common carp (Cyprinus carpio L.) meat fortified with n-3 PUFA. Food and Chemical Toxicology, 139, 111261."
<p>Dataset for "<em>Sobczak M, Panicz R, Eljasik P, Sadowski J, Tórz A, Żochowska-Kujawska J, Barbosa V, Domingues V, Marques A, Dias J. (2020). Quality improvement of common carp (Cyprinus carpio</em><em> L.) meat fortified with n-3 PUFA. </em><strong><em>Food and Chemical Toxicology, 139, 111261. </em></strong><em>DOI: <a href="https://doi.org/10.1016/j.fct.2020.111261">doi.org/10.1016/j.fct.2020.111261</a>"</em></p>
Data on physico-chemical quality and microbial community level physiological profiles of drinking water and freshwater resources in Mega Manila Philippines
<p><strong>Samples of drinking water and freshwater resources in Mega Manila, Philippines were analyzed for physical and chemical quality as well as physiological profiles of microbial communities. Temperature, pH, oxidation-reduction potential, absolute conductivity, resistivity, total dissolved solids, salinity, pressure, and dissolved oxygen were measured using a multi-parameter probe and reported as averages from three biological replicates per sample. Detection and quantification of arsenic were carried out via 3114 B: Hydride Generation AAS method, while analysis for lead and cadmium was carried out via 3111 B: Direct Air-Acetylene Flame method. Detection and quantification of mercury was performed via Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES). Detection and quantification of polyaromatic hydrocarbons (PAHs) and organochlorine pesticides (OCPs) were carried out via Gas Chromatography-Mass Spectrometry (GC-MS) and Gas Chromatography-Electron Capture Detection (GC-ECD). Assessment of the physiological activities of microbial communities in the water samples was done using the Biolog EcoPlateTM system. Eight samples returned less than the minimum detection limit for analysis of heavy metals and no samples contained polyaromatic hydrocarbons and organochlorine pesticides. In terms of microbial community physiological profiles, 66% of the samples (19 out 29) are found to demonstrate consistent increases in AMR and CMD over time. These data can be reused as comparators for retrospective and prospective quality assessments of drinking water and freshwater sources.</strong></p>
Data from: Nectar quality changes the ecological costs of chemically defended pollen
Open the record for dataset details and reuse information.
The Impact of Genicular Nerves Chemical Neurolysis on the Quality of Life of Patients with Advanced Knee Osteoarthritis.
ClinicalTrials.gov study NCT06087601. IPD Sharing: NO. Countries: 1. Publications: 0.
Dataset related to article "Bayesian inference of physico-chemical quality elements of a tropical lagoon Nokoué"
<p>These data are associated with the paper being submitted entitled: <strong>Bayesian inference of tools for assessment of the physicochemical quality of a tropical lagoon.</strong> The data collection covered a 9-year period divided into three sets of years: 2002 - 2006; 2014 -2016; and 2021. All data were acquired from several comparable scientific studies using the same methods (AFNOR, 1997). The first two sets of data had approximately 16% missing data. The imputation of these missing data was performed using the non-parametric "missForest" algorithm for mixed type data. These data sets come from our field work and the field work that led to the following publications: Gnohossou, 2006; Odountan et al., 2019, Zandagba et al., 2016a, 2016b) . A total of 20 parameters were monitored, among which the following 17 physico-chemical parameters were selected: <strong>Water temperature (Temp), transparency (Trans), turbidity (Turb), conductivity (Cond), salinity (Sal), pH, dissolved oxygen (DO), BOD, COD, Kjeldahl nitrogen (TKN), ammonium, nitrates, nitrites, dry organic matter (DM), orthophosphates (Ortho_P), total phosphorus (TP), suspended solids (SM).</strong> The geographical distribution of the sampling points is made in order to cover the whole Nokoué lagoon complex. In total, 20 sampling points were monitored. The parameters were measured according to the four seasons of the year and/or according to the succession of high water, low water, high water and low water transitions. Nine driving forces described as factors likely to induce pressure were retained to explain the physico-chemical characteristics of the Nokoué. The data from the land use maps obtained were used to establish the data related to the land use variables (<strong>OCA, OCU and OCF</strong>). To these 03 land use variables were added 04 other variables capturing the distances between the monitored stations and the 04 tributaries of the Nokoué (<strong>So, MR, DR, TC</strong>). The present observations of these 07 variables could be improved with time depending on the availability of more accurate maps. As natural forcing variables, we have the sampling years, the <strong>seasons (SWS: short wet season or flood period; SDS: short dry season; LWS: long wet season; LDS: long dry season), the mean monthly water level (MAWL) and the mean wind speed (AWS).</strong> These natural forcing variables are important in the context of Nokoué...</p> <p>**Minor modifications**<br> -Zipped data files<br> - Addition of the validation dataset (data_validation.csv)</p> <p> </p>
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International Brain Laboratory public data
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OpenNeuro
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