Related Experiment Video
Updated: Feb 23, 2026

Optimization of the Epimedii Folium Mutton-Oil Processing Technology and Testing Its Effect on Zebrafish Embryonic Development
Published on: March 17, 2023
Quantifying Nonlinear Multiregional Contributions to Ozone and Fine Particles Using an Updated Response Surface
Jia Xing1, Shuxiao Wang1, Bin Zhao1,2
1State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Tsinghua University , Beijing 100084, China.
Abstract:
Tropospheric ozone (O3) and fine particles (PM2.5) come from both local and regional emissions sources. Due to the nonlinearity in the response of O3 and PM2.5 to their precursors, contributions from multiregional sources are challenging to quantify. Here we developed an updated extended response surface modeling technique (ERSMv2.0) to address this challenge. Multiregional contributions were estimated as the sum of three components: (1) the impacts of local chemistry on the formation of the pollutant associated with the change in its precursor levels at the receptor region; (2) regional transport of the pollutant from the source region to the receptor region; and (3) interregional effects among multiple regions, representing the impacts on the contribution from one source region by other source regions. Three components were quantified individually in the case study of Beijing-Tianjin-Hebei using the ERSMv2.0 model. For PM2.5 in most cases, the contribution from local chemistry (i.e., component 1) is greater than the contribution from regional transport (i.e., component 2). However, regional transport is more important for O3. For both O3 and PM2.5, the contribution from regional sources increases during high-pollution episodes, suggesting the importance of joint controls on regional sources for reducing the heavy air pollution.
Related Concept Videos
Response Surface Methodology
The process of RSM involves several key steps:
Mechanistic Models: Compartment Models in Individual and Population Analysis
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Pharmacodynamic Models: Direct Effect Model and Indirect Response Model

