| Literature DB >> 36097490 |
Jared Woollacott1, Wael Alsufyani2, Robert H Beach1, Laura T R Morrison1, Alison Bean de Hernández1, Severin Rakic3, Mashael AlOmran2, Reem F Alsukait4,5, Christopher H Herbst4, Salem AlBalawi2.
Abstract
Air pollution poses major disease burdens globally and accounts for approximately 10% of deaths annually through its contribution to a variety of respiratory, cardiovascular, and other diseases. The burden of disease is particularly acute in Saudi Arabia, where a mix of anthropogenic and natural sources of air pollution threatens public health. Addressing these burdens requires careful study of the costs and effectiveness of available technologies and policies for reducing emissions (mitigation) and avoiding exposure (adaptation). To help evaluate these options, we conduct a semi-systematic literature review of over 3,000 articles published since 2010 that were identified by searches of literature focused on pollution mitigation and pollution adaptation. We identify a wide variety of effective mitigation and adaptation technologies and find that cost-effectiveness information for policy design is highly variable in the case of mitigation, both within and across pollution source categories; or scarce, in the case of adaptation. While pollution control costs are well studied, policy costs differ; these may vary more by location because of factors such as technology operating conditions and behavioral responses to adaptation initiatives, limiting the generalizability of cost-effectiveness information. Moreover, potential cost advantages of multipollutant control policies are likely to depend on the existing mix of pollution sources and controls. While the policy literature generally favors more flexible compliance mechanisms that increase the cost of polluting to reflect its costs to society, important policy design factors include policy co-benefits, distributional concerns, and inter-regional harmonization. In addition to these key themes, we find that further study is needed both to improve the availability of cost information for adaptation interventions and to localize technology and policy cost estimates to the Saudi context.Entities:
Keywords: Adaptation; Air pollution; Air quality; Environment; Mitigation; Public health
Year: 2022 PMID: 36097490 PMCID: PMC9463589 DOI: 10.1016/j.heliyon.2022.e10335
Source DB: PubMed Journal: Heliyon ISSN: 2405-8440
Figure 1Environmental public health process.
Figure 2Search term identification.
Figure 3Literature search PRISMA statement.
Effectiveness of adaptation interventions.
| Intervention | Evidence of Reduced Exposure |
|---|---|
| Face masks | PM2.5 (−30% PM2.5, −71% PM10) |
| Ventilation | PM2.5 (−38%); Black carbon (−68%) |
| PM2.5 (−70%) | |
| Filters | ∼5 DALYs avoided; cost: 10 USD |
| NO2 (−27%) | |
| PM2.5 (−40 μg/m3) | |
| Air quality alerts/indices | Respiratory disease symptoms reported (−16.4%) |
| Hospital admissions for respiratory tract infections (−16%) and pneumonia (−12%) | |
| Significant reduction in cardiovascular disease–related mortality | |
| Traffic separation/transport mode | PM2.5 (4 × reduction); PM10 (13x reduction) |
| Black carbon (−0.37 μg/m3); carbon monoxide (−0.16 ppm) | |
| Significant decrease in PM2.5 exposure |
Mitigation technology costs.
| 2020 USD/Tonne Reduced | ||||||
|---|---|---|---|---|---|---|
| Mitigation Target | Observations | Minimum | Median | Maximum | 25th Percentile | 75th Percentile |
| PM | 24 | −3,133 | 720 | 104,126 | −2,370 | 11,530 |
| SO2 | 10 | 2 | 190 | 19,125 | 41 | 324 |
| NOX | 11 | 0.3957 | 694 | 11,826 | 321 | 5,454 |
| NOX, PM | 12 | 0.82 | 481 | 5,417 | 102 | 1,075 |
| NOX, SO2 | 16 | 4 | 115 | 10,986 | 4 | 388 |
| PM, SO2 | 2 | 204 | 2,705 | 5,206 | 1,454 | 3,956 |
Sources: Authors’ compilation of values reported in literature (Ammar and Seddiek, 2017; T. L. Chen et al., 2019; Evans, Rojas-Bracho, Hammitt and Dockery, 2021; Galvis et al., 1995; H. Li, Tan, Guo, Zhu and Huang, 2019; F. Liu et al., 2013; Mardones and Saavedra, 2016; Miranda et al., 2016; Nazar et al., 2021; Obara and Li, 2020; Ravina et al., 2020; Shawhan and Picciano, 2019; J. Sun, Schreifels, Wang, Fu and Wang, 2014; F. Tong, Hendrickson, Biehler, Jaramillo and Seki, 2017; Wadud and Khan, 2013; S. Zhang, Worrell, Crijns-Graus, Wagner and Cofala, 2014).
EPA control measure costs (2020 USD per tonne).
| NOX | VOCs | PM | SO2 | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Low | Median | High | Low | Median | High | Low | Median | High | Low | Median | High | |
| Point & Non-Point | 0 | 10,557 | 21,114 | −2,345 | 21,539 | 45,424 | 38 | 48,085 | 96,132 | 226 | 40,444 | 80,662 |
| Mobile Sources | 0 | 44,461 | 88,922 | 0 | 4,033 | 8,066 | 0 | 12,736 | 25,472 | n.a. | .n.a. | n.a. |
Source:US EPA (2022).
Note: NOx = oxides of nitrogen; PM = particulate matter; SO2 = sulfur dioxide; VOCs = volatile organic compounds.
Figure 4Technology Costs (USD/ton) by Pollutant from Literature; Sources: Authors’ compilation of values reported in literature (Ammar and Seddiek, 2017; T. L. Chen et al., 2019; Evans et al., 2021; Galvis et al., 1995; H.H. Li et al., 2019; F. Liu et al., 2013; Mardones and Saavedra, 2016; Miranda et al., 2016; Nazar et al., 2021; Obara and Li, 2020; Ravina et al., 2020; Shawhan and Picciano, 2019; J. Sun et al., 2014; F. Tong et al., 2017; Wadud and Khan, 2013; S. Zhang et al., 2014).
Figure 5Marginal Abatement Cost of NOX by Sector and Cost Data Availability. Note: K + OM indicates observations that also include capital and operations and maintenance cost information. ICE = internal combustion engine; NG = natural gas; ICI = industrial, commercial, and institutional. Ranges for horizontal axes vary. Conversion to metric tonnes is USD/ton ∗ 1.1023 = USD/tonne; Source: Click or tap here to enter text (RTI International, 2020, Figure 2).
Mitigation policy costs from literature review.
| 2020 USD/Tonne reduced | ||||||
|---|---|---|---|---|---|---|
| Obs. | Minimum | Median | Maximum | 25th Percentile | 75th Percentile | |
| PM | 11 | 167 | 71,872 | 34 | 6,727 | |
| SO2 | 2 | 780 | 68,840 | 627 | 2,792 | |
| NOX | 0 | 1,550 | 146,739 | 354 | 4,552 | |
| NO2, SO2 | 234 | 3,773 | 15,688 | 3,245 | 13,338 | |
| PM, NOX | -901 | 4,252 | 10,581 | 1,676 | 7,417 | |
| PM, NOX, SO2 | 77 | 358 | 2,731 | 186 | 1,186 | |
| PM, NOX, SO2, VOCs | 1,139 | 1,139 | 1,139 | 1,139 | 1,139 | |
| PM, NOX, SOX | 4 | 9,051 | 115,433 | 17 | 42,419 | |
| PM, NOX, SOX, VOCs | 2,058 | 2,203 | 2,349 | 2,131 | 2,276 | |
| PM, NOX, VOCs | 15 | 3,364 | 6,713 | 1,690 | 5,039 | |
| PM, NOX, HC, CO | 791 | 1,704 | 2,617 | 1,248 | 2,160 | |
| PM, SO2 | 0.31 | 8 | 286 | 2 | 34 | |
Sources: Authors’ compilation of values from literature (F. Chen, Yamashita, Kurokawa and Klimont, 2015; Chiesa et al., 2014; Fowlie et al., 2012; Guo et al., 2018; Hasanbeigi et al., 2013; Howard et al., 2019; Lai et al., 2020; N.N. Li et al., 2019; H. Liu, Meng, et al., 2018; Mardones and Cabello, 2019; McDonald-Buller et al., 2016; Miranda et al., 2016; Peng et al., 2019; Pinchasik et al., 2020; Raff and Walter, 2019; Relvas and Miranda, 2018; Rodgers et al., 2019; Sanderson et al., 2013; Shih and Tseng, 2014; L. Sun et al., 2012; Taksibi et al., 2020; K. Wang et al., 2020; L. Wang et al., 2016; Sheng Wang, Qing, Wang and Li, 2018; Shijie Wang et al., 2019; Xiao et al., 2019; Xu et al., 2021; Zhou et al., 2019).
Note: CO = carbon monoxide; NOx = oxides of nitrogen; PM = particulate matter; SO2 = sulfur dioxide; VOCs = volatile organic compounds; HC = hydrocarbons.