Assessing the temporal-spatial dynamic reduction in ecosystem services caused by air pollution: A near-real-time data perspective

https://doi.org/10.1016/j.resconrec.2022.106205Get rights and content

Highlight

  • This study assesses the impacts of near-real-time air pollution on ES.

  • It has differences in the reduction in ES based on near-real-time and average data.

  • The cities with better air quality have larger differences in the reduction in ES.

  • The near-real-time reduction in ES can reveal potential ecological risks.

  • This study can facilitate more high time-efficient ecosystem management.

Abstract

Air pollution is impacting ecosystem services (ES). This paper proposes an emergy-based method framework to assess the dynamic impacts of near-real-time air pollution on ES at different temporal-spatial scales. The paper presents the cases of Shenzhen, Shanghai, Beijing and Baoding, China, investigates the impacts of air pollution on the ES in 2020. In particular, we compared the reduction in ES evaluated using either average (R1) or near-real-time (R2) air pollution data. The results indicate that the differences between R1 and R2 range from about 9% (for Baoding) to almost 45% (for Shenzhen), thus implying the underestimation of the impacts of air pollution on ES based on average data. The ratios of the reduction in ES to the average ES per hour are near-real-time dynamic, with the values of 0–231.62%, 0–59.42%, 0–50.51%, and 0–35.26% in Shanghai, Baoding, Beijing and Shenzhen respectively. Although the air quality in Shanghai is aggregately better than that in Baoding in 2020, the reduction in ES caused by air pollution in Shanghai is more than twice as that of in Baoding, emphasizing the necessity of investigating the impacts of near-real-time air pollution to properly reveal and assess the potential ecological risks. The ratio of the annual total reduction in ES to the annual total ES, near-real-time reduction in ES to the average ES per hour, and near-real-time reduction in ES to the near-real-time ES in Beijing are 7.22%, 0–50.51% and 0–219.27% respectively, highlighting the significance of near-real-time monitoring of air pollution and ES to reveal the potential or hidden impacts of air pollution on ES, and further to facilitate more high time-efficient and fine ecosystem management and conservation.

Introduction

Ecosystem services (ES) refer to the benefits people obtain from ecosystems (MA The Millennium Ecosystem Assessment 2005). Ecosystems are dynamic entities, and it has a confirmed, yet incomplete evidence, that variations of climate change and air pollution being made are growing the probability of nonlinear and abrupt changes in ecosystems, with significant consequences for human well-being (Xing et al., 2020). Substantial studies have assessed overall annual ES (Ouyang et al., 2020; Ouyang et al., 2016; Yang et al., 2019a, 2020), yet hour-data with detail for air pollution capturing the precise timing of their impacts on ES at different spatial scales are still lacking. This may lead to an overestimation of ES, and subsequently ignore the potential reduction in ES supplied to human well-being. In addition to the differences between near-real-time air pollution data and average data, annual ecological parameters that have historically been applied to assess ecosystem services lag reality by several years, are thus not yet available to assess the near-real-time ES and the reduction in ES caused by air pollution. It may lead to lagging management of ecosystems. This has inspired many efforts to more currently assess the impacts of air pollution on ES.

Air pollution plays an increasing role in affecting ES (Jones et al., 2012; Molina et al., 2008; Wang et al., 2020). Human health and ecosystem quality are generally the two widely used indicators to measure the impacts of air pollution (Manisalidis et al., 2020). The influences on human health are well recorded and substantial researches have pursued to assess the influences via assessing correlated mortality and morbidity of influenced populations (Dedoussi et al., 2020; Miri et al., 2016). Moreover, air pollution has substantial impacts on ecosystem quality via several processes, including eutrophication, acidification of soils and freshwaters and direct toxic impacts of ground level ozone (Jones et al., 2012). Goedkoop and Spriensma (2001) proposed a “bottom-up” method to measure human health and ecosystem quality losses caused by pollution using Disability Adjusted Life Years (DALYs) and Potentially Disappeared Fraction (PDF) of species respectively. It can trace the causality chain from source of air pollutant emissions to variations in air quality and consecutive effects to receivers including human health, ecosystem quality and ES (Hay et al., 2017; Kyu et al., 2018; Murray et al., 2013). Several studies have applied DALYs and PDF to measure air purification service by assessing the reduction in damages to human health and ecosystem quality provided by ecosystems (Bayles et al., 2016; G. Liu et al., 2021; Sijtsma et al., 2013; Verones et al., 2017). Following these studies, DALYs and PDF are selected to assess the reduction in ES caused by air pollution, especially concerning the ability of ES to conserve human health and ecosystem quality.

The impacts of air pollution on ES exhibit temporal-spatial dynamics due to near-real-time variations in air pollution. Specifically, it exists temporal (like seasonal and weather) differences when people receive ES (Yao et al., 2021). For example, people generally benefit less ES in autumn and winter due to more frequent air pollution (such as PM10 and PM2.5) than in summer (Cheng et al., 2016; Deng et al., 2020; Pande et al., 2018). As to spatial scale, the spatial distribution of air pollution emission sources is most likely mismatched with ES supply sources. For example, Dedoussi et al. (2020) found, on average, that 41%−53% of air quality-associated premature mortality generated by an American State's emissions occurs outside that State. Meanwhile, epidemiological studies concluded that space-time variation must be accounted to elicit accurate estimates of the impacts of air pollution on human health (Ozkaynak et al., 2013). A few studies have estimated the economic value of environmental effects resulting from variations in emissions of NOx, SO2, NH3 and O3 (Jones et al., 2012). Yet, Jones et al. (2012) assessed air pollution at interannual scale rather than near-real-time scale, applying average pollutant concentrations, without taking into account the spatial context (Jones et al., 2014) and presenting results at a national level rather than addressing site-specific effects (Jones et al., 2012). Hein et al. (2018) assessed the potential impacts of four European air policy scenarios on N deposition and investigated the subsequent effects on three ES supply: carbon sequestration, timber production and biodiversity. Nevertheless, they ignored other air pollutants that also have various impacts on ES supply (Hein et al., 2018). The European Environment Agency (EEA) analyzed the impacts of air pollution on ecosystems (EEA. 2020), yet it still lacks studies investigating the effects on ES. Indeed, few studies have directly and quantitively assessed the impacts of air pollution on temporal-spatial dynamics in ES from a near-real-time data perspective, bringing challenges for fine and efficient ES management. Therefore, this work aims to propose a method to quantitatively assess this type of impacts, to provide technical support for high time-efficient and fine ES monitoring, management and conservation.

On the other hand, ES can be assessed by various methods, such as economic, emergy and InVEST models, etc. (Yang et al., 2019a). Although economic damage is generally assessed using market values, the economic accounting approaches cannot measure intangible costs, like psychological costs (Pascal et al., 2013), because of the lack of market prices for these factors (Feng et al., 2021). Moreover, ES and damages measured by economic methods is not in one system, and it lacks the logic and mechanism of increase and decrease in ES. Direct additions and subtractions usually lead to a damage value larger than inputs value. Hence, the economic method maybe not suitable to account reduction in ES. Emergy method, based on ecological thermodynamics, is the direct or indirect available energy inputs in the process of ecosystem outputs (Odum, 1996). It can track the work of environment that causes changes in ES through a detailed analysis of material flow and energy transfer (Odum, 1996), and reflect the impacts of pollution on human health and ecosystem quality (Liu et al., 2011). The method has been widely applied to assess forest (Campbell et al., 2012; Yang et al., 2018), grassland (Yang et al., 2020), aquatic (Yang et al., 2019a), coastal, marine ES (Yang et al., 2019b) and biodiversity (Yang et al., 2021). Reduction in ES generated by air pollution is actual the capability degradation of ES to reduce damages to human health and ecosystem quality resulted from air pollution, and is the decrease in ecosystem energy outputs. Additionally, emergy method is able to model ecosystem structure, process and function (G. Liu et al., 2021; Lu et al., 2015). Specifically, in emergy theory, the ecosystem is considered a system governed by the laws of thermodynamics (Odum, 1996). Emergy method could quantitatively describe an ecosystem's components and the relationships among them (Brown et al., 2006; Campbell and Tilley, 2016), and measure the functioning of an ecosystem by assessing the incoming and outgoing of energy and matter that pass through its borders into and from the environment (Nadalini et al., 2021). Therefore, emergy method is more appropriate to account reduction in ES caused by pollution.

Section snippets

Emergy-based ES accounting method framework

Emergy-based ES accounting method framework mainly includes four steps: step 1, identify ecosystem types; step 2: identify ES types; step 3: establish ES accounting techniques; step 4: establish summation principle of total ES. In step 1, the ecosystems investigated here include three types: woodland (including forest, shrub, sparse woodland, other woodland), grassland (high, moderate, low coverage grassland) and aquatic (wetland, lake, reservoir or pond, river) ecosystems. In step 2, ES types

Characteristics of case areas

The Ministry of Ecology and Environment of China announced the air quality ranking of China's 168 key cities in 2020 on January 15, 2021, and presented the specific top and bottom 20 cities. This study selected Shenzhen (rank 6th), Baoding (rank 152th), Shanghai and Beijing as the representatives of the top 20, bottom 20 and 21–148 ranking cities respectively as cases, to measure the potential effects of air pollution on cities’ ES and the temporal-spatial variations. Their location is shown in

Proportion of reduction in ES to the total ES

The ratio of the reduction in ES caused by air pollution to the total ES varies with hours due to the near-real-time air pollution and ecosystem services. For example, the ratios of annual total reduction to total ES, near-real-time reduction to average ES per hour, and near-real-time reduction to the near-real-time ES in Beijing are 7.22%, 0–50.51% and 0–219.27%, respectively. This underlines the significance of near-real-time monitoring of air pollution and ecosystem services in order to

Conclusions

Air pollution has significant impacts on ecosystem services, with subsequent consequences of human health and ecosystem quality. It varies in real time, calling for high-temporal resolution and accurate monitoring of its impacts on ecosystem services to informatively and efficiently guide people outdoor activities and ecosystem management and conservation. Few studies have directly and quantitively assessed the temporal- spatial dynamic impacts of real-time air pollution on ecosystem services.

Credit author statement

ZF Yang and GY Liu were responsible for overall project supervision, conceptualization, project management, and final draft writing, review and editing; Q Yang and GY Liu contributed to methodology development, conducted validation, and contributed to the writing of early drafts; HY Zhao, C Liu, Y Chen and F Gonella contributed to the data curation and revision checking.

Supporting Information

Ecosystem services calculation processes (S1), the frequency distribution of the ratio of R1 to R2 (Figure S1).

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

This work is supported by the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (2019ZT08L213), Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) (GML2019ZD0403), and the National Key Natural Science Foundation of China (52100212; 52070021).

References (53)

  • F.J. Sijtsma et al.

    Beyond monetary measurement: how to evaluate projects and policies using the ecosystem services framework

    Environ. Sci. Policy

    (2013)
  • J.A. Slingsby et al.

    Near-real time forecasting and change detection for an open ecosystem with complex natural dynamics

    ISPRS J. Photogramm. Remote Sens.

    (2020)
  • F. Verones et al.

    LCIA framework and cross-cutting issues guidance within the UNEPSETAC life cycle initiative

    J. Clean. Prod.

    (2017)
  • Q. Yang et al.

    Development of a new framework for non-monetary accounting on ecosystem services valuation

    Ecosyst. Serv.

    (2018)
  • Q. Yang et al.

    Emergy-based accounting method for aquatic ecosystem services valuation: a case of China

    J. Clean. Prod.

    (2019)
  • Q. Yang et al.

    Donor-side evaluation of coastal and marine ecosystem services

    Water Res.

    (2019)
  • Q. Yang et al.

    Emergy-based ecosystem services valuation and classification management applied to China's grasslands

    Ecosyst. Serv.

    (2020)
  • Q. Yang et al.

    Three dimensions of biodiversity: new perspectives and methods

    Ecol. Indic.

    (2021)
  • Z. Zhu

    Change detection using landsat time series: a review of frequencies, preprocessing, algorithms, and applications

    ISPRS J. Photogramm. Remote Sens.

    (2017)
  • Aristotle. 1908. Metaphysics. translated by W. D. Ross. Book VIII, 1045a....
  • B.R. Bayles et al.

    Ecosystem services connect environmental change to human health outcomes

    Ecohealth

    (2016)
  • M.T. Brown et al.

    Species diversity in the Florida Everglades, USA: a systems approach to calculating biodiversity

    Aquat. Sci.

    (2006)
  • E.T. Campbell et al.

    Environmental accounting of natural capital and ecosystem services for the US national forest system

    Environ. Dev. Sustain.

    (2012)
  • I.C. Dedoussi et al.

    Premature mortality related to United States cross-state air pollution

    Nature

    (2020)
  • C. Deng et al.

    Environ. Sci. Technol.

    (2020)
  • European Environment Agency (EEA). 2020. Air quality in Europe-2020 report. No...
  • Cited by (6)

    • Exploring complex place-based coevolution of ecosystem and human activities: A case study of Qilian Mountain area in China

      2022, International Journal of Applied Earth Observation and Geoinformation
      Citation Excerpt :

      The independent variable, X, indicates the ability of the RSCEI–HFI CC level to detect influencing factors. Because pollution discharging and logging are prohibited in the protected area, we used the population density, per capita GDP, transportation accessibility, and digital elevation modeling as indicators to measure HAs and the influences of the natural environment (Chen, 2021; Yang et al., 2022). The dependent variable, Y, refers to the RSCEI–HFI CC level.

    View full text