Air pollution-oriented ecological risk assessment in Xiamen city, China

ABSTRACT

Urban energy consumption is one of the most important causes of air pollution. Air pollution-oriented ecological risk assessment is of great significance to the promotion of urban environmental protection. This paper focuses on ecological risk in Xiamen city caused by air pollutant discharge from urban energy consumption. The Long-range Energy Alternatives Planning model was used to establish two scenarios of energy consumption in Xiamen city, and based on different scenarios, we estimated urban energy consumption and discharge quantity of air pollutant (DQAP). A box model and an expert scoring method were used to calculate the air pollution burden (APB) of SO₂, NO₂, CO, PM₁₀ and PM_2.5 and to obtain the probabilities of different air pollution loads. An ecological risk assessment model was developed and utilized to predict Xiamen city’s ecological risks in 2020. The results showed that under an energy-saving scenario, the ecological risks for PM_2.5, SO₂ and NO₂ are high, whereas the ecological risks for CO and PM₁₀ are low. Under a baseline scenario, the ecological risks for PM_2.5, SO₂ and NO₂ are moderate, whereas the ecological risks for CO and PM₁₀ are low. In addition, the APB of SO₂, NO₂, CO, and PM_2.5, but not of PM₁₀, is predicted to rise. In the simulation, energy generation from coal is the main source of air pollution. Although the DQAP from automobiles is not high, it is predicted to rise year-on-year. In summary, the ecological risk due to pollution in Xiamen city is high, and the main pollutants are SO₂, NO₂ and PM_2.5.

KEYWORDS:

• Air pollution
• energy consumption
• ecological risk
• air pollution burden
• Xiamen city

Acknowledgement

This work was supported by the National Key R&D Program of China (2017YFF0207301), the National Natural Science Foundation of China (71533003) and Fujian Science & Technology Program (2017Y0083).

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Key R&D Program of China (2017YFF0207301), the National Natural Science Foundation of China (71533003), Fujian Science & Technology Program (2017Y0083) and Fujian Natural Science Foundation (2016J01200).