Measurement Report: Rapid changes of chemical characteristics and health risks for high time-resolved trace elements in PM2.5 in a typical industrial city response to stringent clean air actions
Abstract. Atmospheric trace metals entail significant damages in human health and ecosystem safety, and thus a series of clean air actions have been implemented to decrease the ambient element concentrations. Unfortunately, the impact of these emission control measures on element concentrations in fine particles remained poorly understood. In our study, the random forest (RF) model was applied to distinguish the effects of emission and meteorology to trace elements in PM2.5 in a typical industrial city named Tangshan based on a three-year (2017–2020) hourly field observation. The result suggested that the clean air actions have facilitated the dramatic decreases of the deweathered concentrations of Ga, Co, Pb, Zn, and As by 72 %, 67 %, 62 %, 59 %, and 54 %, respectively. It is attributable to the strict implementation of “coal to gas” strategies and optimization of industrial structure and layout. However, the deweathered levels of Ca (8.3 %), Cr (18.5 %), and Fe (23 %) only displayed minor decreases, indicating that the emission control measures for ferrous metal smelting and vehicle emission were not very effective. The positive matrix factorization (PMF) results suggested that the contribution ratios of biomass burning, non-ferrous metal smelting, coal combustion, ferrous metal smelting, heavy oil combustion, and traffic-related dust changed from 5 %, 17 %, 20 %, 15 %, 9 %, and 34 % to 7 %, 13 %, 15 %, 14 %, 10 %, and 41 %, respectively. To date, no significant noncarcinogenic and carcinogenic risks were observed for all of the elements, while both of As and Pb still showed relatively high health damages. It was proposed to further cut down the combustion-related emissions (e.g., As and Pb) because it showed the highest marginal health benefits. Besides, the control of traffic-related emissions might be a key abatement strategy to facilitate the reduction of elements in fine particles.
Rui Li et al.
Status: final response (author comments only)
RC1: 'Comment on egusphere-2022-848', Anonymous Referee #1, 08 Nov 2022
- AC1: 'Reply on RC1', Rui Li, 15 Dec 2022
RC2: 'Comment on egusphere-2022-848', Anonymous Referee #2, 16 Nov 2022
- AC2: 'Reply on RC2', Rui Li, 15 Dec 2022
Rui Li et al.
Data for “Measurement Report: Rapid changes of chemical characteristics and health risks for high time-resolved trace elements in PM2.5 in a typical industrial city in response to stringent clean air actions” https://zenodo.org/record/7031975#.Ywys8cjfmfU
Rui Li et al.
Viewed (geographical distribution)
The authors study the changes of chemical characteristics and health risks for high time-resolved trace elements in PM2.5 in a typical industrial city of China and the changes during three years due to clean air actions. They separated the effects due to meteorology from those due to the
control measures by the use of a random forest (RF) model. The subject is interesting and the use of 1-hour resolution data is not still widespread. However the article can be published only after major revisions.
- All the work is based on the analysis of the elemental concentration with 1-h resolution. At least in the supplementary material some graphs presenting the trend of the concentration of some elements over the three years should be presented
Lines 58-59. More references should be added; many articles dealing with elemental concentrations also in Europe or USA can be found in the literature
L 77: Dall’Osto et al. (2013) is a good example of application of PIXE, but it was not the first one (see. e.g. P.Prati et al, Source apportionment near a steel plant in Genoa (Italy) by continuous aerosol sampling and PIXE analysis, Atmospheric Environment, 2000, 34(19), pp. 3149–3157 or A. D'Alessandro et al., Hourly elemental composition and sources identification of fine and coarse PM10 particulate matter in four Italian towns, Journal of Aerosol Science, 2003, 34(2), pp. 243–259)
L 173: please use the extended name before introducing the acronym (RMSE, MAE)
L 203: The authors should add in the supplementary material the percentage of the elements reconstructed by PMF; it is necessary to assess the quality of the PMF analysis
L 228: How was the total mass measured? The value 5.7% is the average of the hourly or daily values or something else?
L 332: “Such a small change can produce the observed effects?
L 338-340: What about WD? It should be even more important than WS
Paragraph 3.4 The impact of clean air policy on source apportionment of trace elements:
Do the authors believe that the source profiles change over the years? From the data (fig. S4-S6) it does not seem so. The absolute source contribution both to the elements or to the total mass change because the emissions are reduced. I don't understand why the authors don't perform PMF analysis putting all the years together. This would immediately allow to see how the contribution of the different sources is reduced over the years even without having the total PM2.5mass and how the absolute contribution of the different sources to the elements vary. Instead, it is difficult to assess the influence of clean air policies only by looking at fig.7, where the average contributions of the six sources to the total mass concentrations of metals in PM2.5 is reported, which means a percentage contribution. The reduction of the percentage contribution of one source can produce an apparent increase in the percentage contribution of another source, even if the absolute contribution of that source is decreasing! Therefore, in my opinion, it would be more interesting to look at the source trend along the 3 years. The deweathering procedure can be performed also to these data.
L 345: The use of the proper experimental error is mandatory in PMF analysis. How were the experimental uncertainties on deweathered concentrations calculated?
L 368-371(but the same is true for other sources). The identification of the origin of the sources based only on back-trajectories is too qualitative. Since the authors have wind data, they should produce element/source wind polar plots like the ones e.g. reported in fig 3 c of Y. Chen et al.: Simultaneous measurements of urban and rural particles in Beijing, Atmos. Chem. Phys., 20, 9231–9247, 2020
L411-412: The authors must show in the supplementary material the average day (average concentration for each hour of the day like the ones e.g. reported in fig 3 b of Y. Chen et al.: Simultaneous measurements of urban and rural particles in Beijing, Atmos. Chem. Phys., 20, 9231–9247, 2020) at least for Ca, Fe, Zn, to see the rush hour peaks.
L 422-423: I would not define the decreases as dramatic. The experimental uncertainties in source contribution should be also taken into account
L 426: A change of 1% is within the experimental uncertainties due to the fitting procedure, I would not use the word “increase”
L 437-443: I agree with the authors that hourly data can give more valuable information regarding the health risk. However, I do not understand how they calculate the risk from the hourly data without averaging their data at least on a daily scale. EF represents the annual exposure frequency (d y−1), ADD is the daily intake (mg/kg/day) of trace metals, all these are quantities averaged at least on a daily base.