Dataset Information

Variability analysis in PM2.5 monitoring.

ABSTRACT: United States Environmental Protection Agency (US EPA) and Central Pollution Control Board (CPCB) are two major air quality monitoring agencies in India that measure the concentration of particulate matter of size up to 2.5 ?m (PM2.5). PM2.5 study over southern Asia has significance from the environment and ecosystem viewpoint (Abdullah et al.,2007; Dockery and Stone, 2007). In order to raise alert and controlling of pollutants, not only forecasting but the accuracy of forecasting has attracted attentions from various departments of research and air quality monitoring agencies. Quest for reducing error in forecasting has never come to pause. The precursor in forecasting is data monitoring. Keeping in focus the initial phase of data analysis, PM2.5 concentration was collected from both agencies within an area of radius 3.1 miles for the year 2016. Using the data, variability analysis is carried out for the efficiency of vital environment protection agencies.

SUBMITTER: Bhardwaj R

PROVIDER: S-EPMC6468191 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Variability analysis in PM2.5 monitoring.

Bhardwaj Rashmi R Pruthi Dimple D

Data in brief 20190314

United States Environmental Protection Agency (US EPA) and Central Pollution Control Board (CPCB) are two major air quality monitoring agencies in India that measure the concentration of particulate matter of size up to 2.5 μm (PM2.5). PM2.5 study over southern Asia has significance from the environment and ecosystem viewpoint (Abdullah et al.,2007; Dockery and Stone, 2007). In order to raise alert and controlling of pollutants, not only forecasting but the accuracy of forecasting has attracted ...[more]

PMID: 31016211

Similar Datasets

Project description:To identify the sources of PM2.5 - bound carbonaceous species and examine the spatial variability of source contributions in the Denver metropolitan area, positive matrix factorization (PMF) was applied to one year of every sixth day ambient PM2.5 compositional data, including elemental carbon (EC), organic carbon (OC), and 32 organic molecular markers, from four sites (two residential and two near-traffic). Statistics (median, inner quantiles and 5th - 95th percentiles range) of factor contributions, expressed as reconstructed carbonaceous mass (EC + OC), were estimated from PMF solutions of replicate datasets generated by using a stationary block bootstrap technique. A seven-factor solution was resolved for a set of data pooled across the four sites, as it gave the most interpretable results and had the highest rate of neural network factor matching (76.9%). Identified factors were primarily associated with high plant wax, summertime emission, diesel vehicle emission, fossil fuel combustion, motor vehicle emission, lubricating oil combustion and wood burning. Pearson correlation coefficients (r) and coefficients of divergence (COD) were used to assess spatial variability of factor contributions. The summertime emission factor exhibited the highest spatial correlation (r = 0.74 - 0.88) and lowest CODs (0.32 - 0.38) among all resolved factors; while the three traffic dominated factors (diesel vehicle emission, motor vehicle emission and lubricating oil combustion) showed lower correlations (r = 0.47 - 0.55) and higher CODs (0.41 - 0.53) on average. Average total EC and OC mass were apportioned to each factor and showed a similar distribution across the four sites. Modeling uncertainties were defined as the 5th - 95th percentile range of the factor contributions derived from valid bootstrap PMF solutions, and were highly correlated with the median factor contribution in each factor (r = 0.77 - 0.98). Source apportionment was also performed on site specific datasets; the results exhibited similar factor profiles and temporal variation in factor contribution as those obtained for the pooled dataset, indicating that the four sites are primarily influenced by similar types of sources. On the other hand, differences were observed in absolute factor contributions between PMF solutions for the pooled versus site-specific datasets, likely due to the large uncertainties in EC and OC factor profiles derived from the site specific datasets with limited numbers of observations.

Project description:The Korea - United States Air Quality Study (May - June 2016) deployed instrumented aircraft and ground-based measurements to elucidate causes of poor air quality related to high ozone and aerosol concentrations in South Korea. This work synthesizes data pertaining to aerosols (specifically, particulate matter with aerodynamic diameters <2.5 micrometers, PM2.5) and conditions leading to violations of South Korean air quality standards (24-hr mean PM2.5 < 35 μg m-3). PM2.5 variability from AirKorea monitors across South Korea is evaluated. Detailed data from the Seoul vicinity are used to interpret factors that contribute to elevated PM2.5. The interplay between meteorology and surface aerosols, contrasting synoptic-scale behavior vs. local influences, is presented. Transboundary transport from upwind sources, vertical mixing and containment of aerosols, and local production of secondary aerosols are discussed. Two meteorological periods are probed for drivers of elevated PM2.5. Clear, dry conditions, with limited transport (Stagnant period), promoted photochemical production of secondary organic aerosol from locally emitted precursors. Cloudy humid conditions fostered rapid heterogeneous secondary inorganic aerosol production from local and transported emissions (Transport/Haze period), likely driven by a positive feedback mechanism where water uptake by aerosols increased gas-to-particle partitioning that increased water uptake. Further, clouds reduced solar insolation, suppressing mixing, exacerbating PM2.5 accumulation in a shallow boundary layer. The combination of factors contributing to enhanced PM2.5 is challenging to model, complicating quantification of contributions to PM2.5 from local versus upwind precursors and production. We recommend co-locating additional continuous measurements at a few AirKorea sites across South Korea to help resolve this and other outstanding questions: carbon monoxide/carbon dioxide (transboundary transport tracer), boundary layer height (surface PM2.5 mixing depth), and aerosol composition with aerosol liquid water (meteorologically-dependent secondary production). These data would aid future research to refine emissions targets to further improve South Korean PM2.5 air quality.

Project description:Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 (n = 22), type 2 diabetes (n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values. We calculated Poincaré plot measures (SD1, SD2), shape (SFE) and area of the fitting ellipse (AFE), multiscale entropy (MSE) index, and detrended fluctuation exponents (?1, ?2). The glycemic variability metrics were the coefficient of variation (%CV) and standard deviation. Time of glucose readings in the target range (TIR) defined the quality of glycemic control. The Poincaré plot indices and ? exponents were higher (p < 0.05) in type 1 than in the type 2 diabetes; SD1 (mmol/l): 1.64 ± 0.39 vs. 0.94 ± 0.35, SD2 (mmol/l): 4.06 ± 0.99 vs. 2.12 ± 1.04, AFE (mmol2/l2): 21.71 ± 9.82 vs. 7.25 ± 5.92, and ?1: 1.94 ± 0.12 vs. 1.75 ± 0.12, ?2: 1.38 ± 0.11 vs. 1.30 ± 0.15. The MSE index decreased consistently from the non-diabetic to the type 1 diabetic group (5.31 ± 1.10 vs. 3.29 ± 0.83, p < 0.001); higher indices correlated with lower %CV values (r = -0.313, p < 0.001). In a subgroup of type 1 diabetes patients, insulin pump therapy significantly decreased SD1 (-0.85 mmol/l), SD2 (-1.90 mmol/l), and AFE (-16.59 mmol2/l2), concomitantly with %CV (-15.60). The MSE index declined from 3.09 ± 0.94 to 1.93 ± 0.40 (p = 0.001), whereas the exponents ?1 and ?2 did not. On multivariate regression analyses, SD1, SD2, SFE, and AFE emerged as dominant predictors of TIR (? = -0.78, -1.00, -0.29, and -0.58) but %CV as a minor one, though ?1 and MSE failed. In the regression models, including SFE, AFE, and ?2 (? = -0.32), %CV was not a significant predictor. Poincaré plot descriptors provide additional information to conventional variability metrics and may complement assessment of glycemia, but complexity measures produce mixed results.

Dataset Information

Variability analysis in PM2.5 monitoring.

Publications

Variability analysis in PM2.5 monitoring.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets