Identifying the main factors influencing PM 2.5 concentrations is critical for the control and
management of elevated atmospheric pollution. Although the impact of natural and land use …

Spatially varying coefficient models are a classical tool to explore the spatial nonstationarity
of a regression relationship for spatial data. In this paper, we study the estimation and …

Particulate matter (PM) has emerged as a primary air quality concern due to its substantial
impact on human health. Many recent research works suggest that PM 2.5 concentrations …

Temperature has been identified as a key control over particulate matter of diameters
smaller than 2.5 μm (PM2. 5); however, the mechanisms controlling this phenomenon in …

Identifying spatial clusters of different regression coefficients is a useful tool for discerning
the distinctive relationship between a response and covariates in space. Most of the existing …

Air quality is critical for public health. Residents rely chiefly on government agencies such as
the Environmental Protection Agency (EPA) in the United States to establish standards for …

Nowadays, multivariate functional data are frequently observed in many scientific fields, and
the estimation of quantiles of these data is essential in data analysis. Unlike in the univariate …

The prevalence of spatially referenced multivariate data has impelled researchers to
develop procedures for joint modeling of multiple spatial processes. This ordinarily involves …

Recently, due to accelerations in urban and industrial development, the health impact of air
pollution has become a topic of key concern. Of the various forms of air pollution, fine …

When performing spatial regression analysis in environmental data applications, spatial
heterogeneity in the regression coefficients is often observed. Spatially varying coefficient …