AIR analysisCUMULATIVE distribution functionSTATISTICSThis paper aims to create a new probability distribution and conducts statistical analysis on air quality dataset from Kathmandu. Using this innovative distribution, we have studied the ground reality of air quality conditions of Kathmandu, Nepal. In ...
Positive matrix factorization (PMF) was applied to air quality and temperature data collected as part of the Program for Research on Oxidants: Photochemistry, Emissions, and Transport 1997 summer measurement campaign. Unlike more conventional methods of factor analysis such as principal component analysis...
This dataset would be the first high-resolution air quality reanalysis dataset in China that can simultaneously provide the surface concentrations of six conventional air pollutants in China, which should be of great value for many studies, such as the assessment of health impacts of air pollution,...
Experimental work has been carried out using existing machine learning techniques and proposed method on the air quality dataset of Delhi. It has been observed that the proposed method extracts wind speed, carbon monoxide and nitrogen dioxide as the key parameters and further accuracy of this method...
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inc
Our analysis contributes to a broader and emerging literature that documents the complex interactions between climate policies, overall air quality benefits, and pollution disparities. Our research reveals two insights about this complex interaction: first, we find that different decarbonization pathways can...
In this paper, the ambient air quality data in 2013, Shijiazhuang, 7 automatic monitoring points drawn from the analysis of the primary pollutant in Shijiazhuang PM2.5 and PM10. The trend of sulfur dioxide, nitrogen dioxide, PM2.5 and PM10 indexes from an up-parabola type, high in winter, ...
As a globally modeled dataset, some uncertainty is to be expected, though sensitivity tests suggest good agreement with ground measurement32. More spatially nuanced analysis—for example, at a neighborhood or street level—would require alternative data based on local measures. It should also be note...
Explore and run machine learning code with Kaggle Notebooks | Using data from UCI ML Air Quality Dataset
As in the Guro-gu air quality dataset, we eliminate 20% of the test data values and evaluate the imputation performance for the eliminated values. The missing rates of test data before the elimination and after the elimination are 16.1% and 32.9%, respectively. 2.2. Imputation Method We ...