the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Oct 2022
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.
-
Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
-
Preprint
(2037 KB)
-
Supplement
(2342 KB)
-
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2037 KB) - Metadata XML
-
Supplement
(2342 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-848', Anonymous Referee #1, 08 Nov 2022
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.
Comments:
- 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
- 400.402: ref. Moreno et al, says something more complex. At the end they say “Thus there is considerable overlap between V/Ni values in natural mineral dusts and those in emissions from the combustion of refinery-produced materials, and this hinders use of this ratio in pollution source identification.” Therefore the authors can keep the sentence, but they should be more cautious. Furthermore, is 1.2 the V/Ni ratio within the identified source (which can be easily obtained by the PMF analysis) or the ratio between the average concentrations during the sampling period? It is the former one which must be presented
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.
Citation: https://doi.org/10.5194/egusphere-2022-848-RC1 -
AC1: 'Reply on RC1', Rui Li, 15 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-848/egusphere-2022-848-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2022-848', Anonymous Referee #2, 16 Nov 2022
This paper reports occurrence levels of elements in PM2.5 in Tangshan China, analyzes the temporal variations and evolution of PM2.5-associated elements, assesses the contributions of emission and meteorology to these species, apportions sources of elements during the whole period and evaluated influence of pollution control measures on the changes of carcinogenic and non-carcinogenic risks. This manuscript tries to represent the observation results and address relevant scientific questions. The scientific methods and assumptions are almost valid and outlined so that conclusions are reached. The description of experiments and calculations are shown. Observed phenomena as presented in the text and SIs have been described in detail. However, there are some hypotheses in the manuscript lacking of crucial evidential data to support. The link between the observed data and suggested implications is not strong. I do not recommend the publication of this article in its current form except some points.
- The authors should go beyond reporting the measurements, present informative interpretations of the data and provide worldwide implication rather than local attention.
- Please introduce clean air actions and pollution control measures in detail. Which control measures are used to improve air quality?
- Atmospheric trace metals include nutrient elements and hazard elements. The nutrient elements are beneficial for ecosystem safety.
- A comparative analysis was performed on the concentration of the trace metals in PM5 observed in different cities. A scientific summary is necessary. Moreover, considering that the analytical methods and sampling duration were probably different for the available data set from the measurement and the literature review and uncertainty existed, the difference in the concentration of trace elements should be obtained based on statistical analysis.
- Uncertainty on results of the random forest (RF) model (deweather) should be further shown in detail. Detailed input should be introduced into the model in detail.
- Sufficient information was not provided by authors to validate the quality of the data. Being a field/experimental study, I am surprised by the lack of details on the data quality assessment and/or quality control methods, instrument calibration, how uncertainties are estimated. How to obtain meteorological parameters and PM5?
- No legends in some figures were given. Figure s has too crowded X-axe. Please modify them.
- Why As had relatively high contribution in factor 1? Why was no vehicular emission identified in the results of source apportionment? As well known, dust (fugitive dust, dust storm, soil dust and road dust) have high or moderate loading of Ca, Cu and Fe.
- The overall carcinogenic properties of mass PM itself should be taken into account. Emissions from gasoline and diesel engines and metal smelting and refining are considered Class 1A carcinogens by IARC; numerous populations studies have demonstrated increased (lung) cancer risk in communities with high urban PM exposures. Thus, the overall cancer risk from mass-based PM is likely to outweigh the risk of a limited set of elements. The authors seem to overinterpret their own risk assessment. If a realistic assessment of the actual health risks was involved, the results would be more meaningful.
- Since RfD and HQ typically have (considerable) uncertainty/safety factors included, a HQ> 1 does not indicate “occurrence of adverse n-c effects” as claimed.
- Open biomass burning has been banned in China after the execution of clean air act since 2013, in particular after 2018. How authors judged that biomass burning or open waste incineration in NCP contributed to the concentrations of trace elements in Tangshan?
Citation: https://doi.org/10.5194/egusphere-2022-848-RC2 -
AC2: 'Reply on RC2', Rui Li, 15 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-848/egusphere-2022-848-AC2-supplement.pdf
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2022-848', Anonymous Referee #1, 08 Nov 2022
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.
Comments:
- 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
- 400.402: ref. Moreno et al, says something more complex. At the end they say “Thus there is considerable overlap between V/Ni values in natural mineral dusts and those in emissions from the combustion of refinery-produced materials, and this hinders use of this ratio in pollution source identification.” Therefore the authors can keep the sentence, but they should be more cautious. Furthermore, is 1.2 the V/Ni ratio within the identified source (which can be easily obtained by the PMF analysis) or the ratio between the average concentrations during the sampling period? It is the former one which must be presented
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.
Citation: https://doi.org/10.5194/egusphere-2022-848-RC1 -
AC1: 'Reply on RC1', Rui Li, 15 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-848/egusphere-2022-848-AC1-supplement.pdf
-
RC2: 'Comment on egusphere-2022-848', Anonymous Referee #2, 16 Nov 2022
This paper reports occurrence levels of elements in PM2.5 in Tangshan China, analyzes the temporal variations and evolution of PM2.5-associated elements, assesses the contributions of emission and meteorology to these species, apportions sources of elements during the whole period and evaluated influence of pollution control measures on the changes of carcinogenic and non-carcinogenic risks. This manuscript tries to represent the observation results and address relevant scientific questions. The scientific methods and assumptions are almost valid and outlined so that conclusions are reached. The description of experiments and calculations are shown. Observed phenomena as presented in the text and SIs have been described in detail. However, there are some hypotheses in the manuscript lacking of crucial evidential data to support. The link between the observed data and suggested implications is not strong. I do not recommend the publication of this article in its current form except some points.
- The authors should go beyond reporting the measurements, present informative interpretations of the data and provide worldwide implication rather than local attention.
- Please introduce clean air actions and pollution control measures in detail. Which control measures are used to improve air quality?
- Atmospheric trace metals include nutrient elements and hazard elements. The nutrient elements are beneficial for ecosystem safety.
- A comparative analysis was performed on the concentration of the trace metals in PM5 observed in different cities. A scientific summary is necessary. Moreover, considering that the analytical methods and sampling duration were probably different for the available data set from the measurement and the literature review and uncertainty existed, the difference in the concentration of trace elements should be obtained based on statistical analysis.
- Uncertainty on results of the random forest (RF) model (deweather) should be further shown in detail. Detailed input should be introduced into the model in detail.
- Sufficient information was not provided by authors to validate the quality of the data. Being a field/experimental study, I am surprised by the lack of details on the data quality assessment and/or quality control methods, instrument calibration, how uncertainties are estimated. How to obtain meteorological parameters and PM5?
- No legends in some figures were given. Figure s has too crowded X-axe. Please modify them.
- Why As had relatively high contribution in factor 1? Why was no vehicular emission identified in the results of source apportionment? As well known, dust (fugitive dust, dust storm, soil dust and road dust) have high or moderate loading of Ca, Cu and Fe.
- The overall carcinogenic properties of mass PM itself should be taken into account. Emissions from gasoline and diesel engines and metal smelting and refining are considered Class 1A carcinogens by IARC; numerous populations studies have demonstrated increased (lung) cancer risk in communities with high urban PM exposures. Thus, the overall cancer risk from mass-based PM is likely to outweigh the risk of a limited set of elements. The authors seem to overinterpret their own risk assessment. If a realistic assessment of the actual health risks was involved, the results would be more meaningful.
- Since RfD and HQ typically have (considerable) uncertainty/safety factors included, a HQ> 1 does not indicate “occurrence of adverse n-c effects” as claimed.
- Open biomass burning has been banned in China after the execution of clean air act since 2013, in particular after 2018. How authors judged that biomass burning or open waste incineration in NCP contributed to the concentrations of trace elements in Tangshan?
Citation: https://doi.org/10.5194/egusphere-2022-848-RC2 -
AC2: 'Reply on RC2', Rui Li, 15 Dec 2022
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2022/egusphere-2022-848/egusphere-2022-848-AC2-supplement.pdf
Peer review completion
Journal article(s) based on this preprint
Data sets
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” Li, R., Peng, M., Zhao, W. D., Wang, G. H., and Hao, J. M. https://zenodo.org/record/7031975#.Ywys8cjfmfU
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 337 | 113 | 18 | 468 | 46 | 4 | 9 |
- HTML: 337
- PDF: 113
- XML: 18
- Total: 468
- Supplement: 46
- BibTeX: 4
- EndNote: 9
Viewed (geographical distribution)
| Country | # | Views | % |
|---|---|---|---|
| China | 1 | 157 | 33 |
| United States of America | 2 | 134 | 28 |
| Germany | 3 | 26 | 5 |
| France | 4 | 13 | 2 |
| Taiwan | 5 | 12 | 2 |
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
- 157
Rui Li
Meng Peng
Weidong Zhao
Gehui Wang
Jiming Hao
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(2037 KB) - Metadata XML
-
Supplement
(2342 KB) - BibTeX
- EndNote
- Final revised paper