the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development of an integrated model framework for multi-air-pollutant exposure assessments in high-density cities and the implications for epidemiological research
Zhiyuan Li
Kin-Fai Ho
Harry Fung Lee
Abstract. Exposure models for some criteria air pollutants have been intensively developed in past research; multi-air-pollutant exposure models, especially for particulate chemical species, have been however overlooked in Asia. Lack of an integrated model framework to calculate multi-air-pollutant exposure hinders the combined exposure assessment and the corresponding health assessment. This work applied the land-use regression (LUR) approach to develop an integrated model framework to estimate annual-average exposures of four major PM10 chemical species as well as four criteria air pollutants of PM10, PM2.5, NO2, and O3 in a typical high-rise and high-density Asian city (Hong Kong, China). Our integrated multi-air-pollutant exposure model framework is capable of explaining 91–97 % of the variability of measured air pollutant concentration, with the leave-one-out cross-validation R2 values ranging from 0.73 to 0.93. Using the model framework, the spatial distribution of the concentration of various air pollutants at a spatial resolution of 500 m was generated. The LUR model-derived spatial distribution maps revealed weak to moderate spatial correlations between the PM10 chemical species and the criteria air pollutants, which may help to distinguish their independent chronic health effects. In addition, further improvements in the development of air pollution exposure models are discussed. This study proposes an integrated model framework for estimating multi-air-pollutant exposure in high-density and high-rise urban areas, serving an important tool for combined exposure assessment and the corresponding epidemiological studies.
- Preprint
(899 KB) -
Supplement
(1556 KB) - BibTeX
- EndNote
Zhiyuan Li et al.
Status: open (until 15 Jun 2023)
-
RC1: 'Comment on egusphere-2023-513', Anonymous Referee #1, 17 May 2023
reply
The authors present results from a land use regression (LUR) framework used to create 500m resolution exposure fields for multiple pollutants, including NO2, O3, total PM1- and PM2.5, and multiple PM10 species. Their LUR models have good predictive performance in leave one out cross validation. I agree with the authors that the fine-scale exposure products may be useful for future exposure and epidemiological studies. I believe, however, that the study does not go far enough in explaining the implications of their results. In particular, I find it is lacking in two areas relevant to the scope of ACP.
First, very little description is given of the meaning behind the variables that are selected for the LUR models, including why they are predictive of the various species and what they may be proxies for. Did any variables that have been found predictive not make sense? Are there co-linearities that may be obscuring the influence of some variables over others? Can we learn something from the predictors chosen that can inform policies to reduce exposure?
Second, there is little description provided of the exposure products themselves. What are the implications for human exposure? It would be useful to select important areas of the city (e.g., an area with high population density), describe which pollutants are predicted to have high concentrations, and offer some suggestions about why.
Specific comments
Abstract: it is important to describe the exposure product in full, including the temporal coverage (i.e., which year?)
Line 81-82: it strikes me as strange to have 3 citations for a sentence describing the geography of Hong Kong.
122-123: it would be helpful to explain more about why lat/lon would account appropriately for transboundary pollution
134-135: please explain “if the direction was as pre-defined”
135: model selection process: please confirm whether the model selection R2 was calculated on the training dataset or the hold out dataset. If the training dataset, is there risk of over-fitting?
135: It seems to me that more flexible machine learning methods may be more adept at capturing nonlinear relationships between environmental predictors and measured pollutants. Is it expected that these variables have a linear relationship with pollutant species? The final paragraph of the manuscript mentions that these are more appropriate with more data, but there is no evidence given or description of how much data is needed.
167: “either comparable to or higher than” makes it sound like a dichotomous variable. I think “generally greater than” would describe this well enough.
176-178: I do not understand how the selection of the predictor variables was related to these other factors. Please clarify.
192: This line refers to a negative z score close to 1. Does this suggest anti-spatial correlation for Cd? More description of this variable is needed
Could the authors provide spatial maps of error at monitor locations? In general, it would be useful to develop estimates of uncertainty on the the same spatial scale as the predictions.
220: is this implying that population density leads to high PM? Can the authors suggest a mechanism here that isn’t explained by the other model covariates?
239-241: I am confused about the difference between the GAS and PM modules. Is it necessary to differentiate beyond the pollutant species names?
257-260: I am not sure what this adds to the discussion. Can the authors be more specific here based on the predictors chosen for the final model?
How was 500m selected as the best resolution for the predictions?
Citation: https://doi.org/10.5194/egusphere-2023-513-RC1
Zhiyuan Li et al.
Zhiyuan Li et al.
Viewed
HTML | XML | Total | Supplement | BibTeX | EndNote | |
---|---|---|---|---|---|---|
226 | 64 | 7 | 297 | 19 | 1 | 1 |
- HTML: 226
- PDF: 64
- XML: 7
- Total: 297
- Supplement: 19
- BibTeX: 1
- EndNote: 1
Viewed (geographical distribution)
Country | # | Views | % |
---|
Total: | 0 |
HTML: | 0 |
PDF: | 0 |
XML: | 0 |
- 1