Through continuous development, China's natural environment protection system (NEPS) has been initially established, and has played an important role in maintaining the balance of the natural ecosystem, protecting natural resources and the ecological environment. This paper constructs the NEPS based on the air quality comprehensive index (AQCI). The purpose and significance of the establishment of the NEPS are discussed, and the control requirements of the planning scope of the NEPS under the AQCI are analyzed; Based on the comprehensive air quality index, this paper mainly discusses four typical technical methods: ecological sensitivity analysis method, environmental capacity control method, acceptable change limit analysis method and map method. The establishment of a NEPS is an important means to maintain regional ecological balance and protect the ecological environment. It is also an important carrier for building an ecological civilization and a beautiful China. It has played an important role in maintaining China's sustainable economic and social development.

Hany Teekaraman. Natural Environment Protection System based on Air Quality Comprehensive Index. Nature Environmental Protection (2020), Vol. 1, Issue 1: 36-45. https://doi.org/10.38007/NEP.2020.010105.

[1] Rajamanickam R, Nagan S. Air Quality Index of Chennai City - A Case Study. Indian Journal of Environmental Protection. (2019) 39(4):345-351.

[2] Lei J. Reforming the Natural Resource Auditing System from the Ecological Civilization Perspective. Chinese Journal of Population, Resources and Environment. (2020) 18(2):87-96. https://doi.org/10.1016/j.cjpre.2019.01.001

[3] Henk W K, Hon K K, Chan P W. Turbulence Intensity Footprints of Built and Natural Environment as Measured by Anemometers at Hong Kong International Airport. Meteorologische Zeitschrift. (2020) 31(1):3-12. 

[4] Dequan L, Jiming L, Xu Z, Jin N H, Xin W, Jing D. Fact: An Air-Ground Communication Framework for Seeding Quality Control of Aircraft. Comput. Syst. Sci. Eng. (2020) 41(2): 539-555.

[5] Sabine S, Eleonora E, Amirpasha M, Mathilde R, Niklas S, Martin G S. Enabling Canonical Analysis Workflows Documented Data Harmonization on Global Air Quality Data. Data Intell. (2020) 4(2): 259-270. https://doi.org/10.1162/dint_a_00130

[6] Haider A H A, Rosdiadee N, Mandeep J S, Nor F A, Azril H, Kentaro I, Takeshi M, Fumihide K, Nordin B R. Low-Altitude-Platform-Based Airborne IoT Network (LAP-AIN) for Water Quality Monitoring in Harsh Tropical Environment. IEEE Internet Things. (2020) 9(20): 20034-20054. 

[7] Kaiser A, Lu Z, Sybil D, Indacochea J E, Didem O, Brian D Z. Machine Learning Model to Predict Welding Quality Using Air-Coupled Acoustic Emission and Weld Inputs. Intell. Manuf. (2020) 33(3): 881-895. https://doi.org/10.1007/s10845-020-01667-x

[8] Ning L, Zhi Y W, Guo D L, Xin Y L, Yue W, Lin Z. MAIC: Metalearning-Based Adaptive In-Field Calibration for IoT Air Quality Monitoring System. IEEE Internet Things J. (2020) 9(17): 15928-15941. 

[9] Aref S, Rahil H, Mahdi M. A Type-2 Fuzzy Time Series Model for Pattern Similarity Analysis: A Case Study on Air Quality Forecasting. IEEE Intell. Syst. (2020) 37(2): 92-102. 

[10] Zeng, Gang, Yang, et al. Study of the Urban Coordinated Development Capability Index in the Yangtze River Economic Belt. Journal of Landscape Research. (2020) v.12 (02):44-51. CNKI:SUN:JLDR.0.2020-02-009

[11] Gregorius A, Alan L. Investigating Software Domain Impact in Requirements Quality Attributes Prediction. J. Inf. Sci. Eng. (2020) 38(2): 295-316. 

[12] Banani G, Zeenat R, Leonidas G A. A Deep Learning based Air Quality Prediction Technique Using Influencing Pollutants of Neighboring Locations in Smart City. J. Univers. Comput. Sci. (2020) 28(8): 799-826. https://doi.org/10.3897/jucs.78884

[13] Scarlet S, Clara B, Ribana R. Explainable Machine Learning Reveals Capabilities, Redundancy, and Limitations of a Geospatial Air Quality Benchmark Dataset. Mach. Learn. Knowl. Extr. (2020) 4(1): 150-171. https://doi.org/10.3390/make4010008

[14] Alexander V S, Evgeniy A D, Sergei A P, Lubov I K, Elena A S. A Supercomputer-Based Modeling System for Short-Term Prediction of Urban Surface Air Quality. Supercomput. Front. Innov. (2020) 9(1): 17-31. https://doi.org/10.14529/jsfi220102

[15] Jasleen K S, Mamta M. Efficient Weighted Naive Bayes Classifiers to Predict Air Quality Index. Earth Sci. Informatics. (2020) 15(1): 541-552. 

[16] Sakae N, Mark B, Daniel D. Effects of Input Data Uncertainties on an Air Traffic Control Difficulty Index. IEICE Trans. Commun. (2020) 104-B(9): 1188-1196. https://doi.org/10.1587/transcom.2020EBP3019

[17] Yu J G, Yu X Z, Jian Z, Hui L, Yu J W. A Fuzzy Multiple Linear Regression Model Based on Meteorological Factors for Air Quality Index Forecast. J. Intell. Fuzzy Syst. (2020) 40(6): 10523-10547. https://doi.org/10.3233/JIFS-201222

[18] Kok S W, Yee J C, Shih Y O, Ying H P. toward Forecasting Future Day Air Pollutant Index in Malaysia. J. Supercomput. (2020) 77(5): 4813-4830. https://doi.org/10.1007/s11227-020-03463-z