生态环境学报
生態環境學報
생태배경학보
ECOLOGY AND ENVIRONMENT
2014年
6期
985-991
,共7页
熊丽君%赵艳佩%黄沈发%林根满%周小凡%吴昊
熊麗君%趙豔珮%黃瀋髮%林根滿%週小凡%吳昊
웅려군%조염패%황침발%림근만%주소범%오호
空气负离子%生态用地%健康效益%主成分分析%多元回归分析
空氣負離子%生態用地%健康效益%主成分分析%多元迴歸分析
공기부리자%생태용지%건강효익%주성분분석%다원회귀분석
negative air ions%ecological health benefit%principal component analysis%multiple regression analysis
空气负离子不仅具有杀菌和清洁空气的功效,还能够增强心肌活力,促进新城代谢,对人体健康有益。因此,为表征生态用地的健康效益,在考虑植被覆盖度、优势物种数、叶面积指数、湿度、周边水域类型、前期降雨事件的基础上,引入空气负离子浓度指标,综合评价生态用地健康效益。以上海市24个生态用地为研究对象,2012年,选取空气质量较好的夏季,对空气负离子、正离子、植被覆盖度、优势物种数、叶面积指数、湿度进行同步监测,同时结合监测点周边水域类型、监测日期的前期降雨特征,利用主成分分析法(PCR)确定影响生态用地健康效益的主要成分,并对24个生态用地的健康效益进行评价。结果表明,在对生态健康效益有利的7项因子中,植被覆盖度在第一主成分中贡献仅略次于湿度,在第二主成分中贡献最大,系数分别为0.689、0.664。在24个生态用地中,海湾森林公园生态健康效益最高,其次是金山岛自然保护区、佘山森林公园,综合得分分别为1035.54、856.87和820.98;上海植物园相对最低,主成分得分为222.28。在此基础上,为探明空气负离子与各影响因子之间的关联性,以空气负离子为因变量,各影响因子为自变量,构建了上海夏季空气负离子浓度与影响因子之间的回归方程,其复相关系数为0.916,修正多重判定系数为0.816,可以表征24个生态用地各影响因素对空气负离子浓度关系81.6%的信息,回归方程检验水平Sig值为0.001,回归关系达极显著水平(P<0.01),可作为上海生态用地夏季空气负离子浓度初步预测的依据。
空氣負離子不僅具有殺菌和清潔空氣的功效,還能夠增彊心肌活力,促進新城代謝,對人體健康有益。因此,為錶徵生態用地的健康效益,在攷慮植被覆蓋度、優勢物種數、葉麵積指數、濕度、週邊水域類型、前期降雨事件的基礎上,引入空氣負離子濃度指標,綜閤評價生態用地健康效益。以上海市24箇生態用地為研究對象,2012年,選取空氣質量較好的夏季,對空氣負離子、正離子、植被覆蓋度、優勢物種數、葉麵積指數、濕度進行同步鑑測,同時結閤鑑測點週邊水域類型、鑑測日期的前期降雨特徵,利用主成分分析法(PCR)確定影響生態用地健康效益的主要成分,併對24箇生態用地的健康效益進行評價。結果錶明,在對生態健康效益有利的7項因子中,植被覆蓋度在第一主成分中貢獻僅略次于濕度,在第二主成分中貢獻最大,繫數分彆為0.689、0.664。在24箇生態用地中,海灣森林公園生態健康效益最高,其次是金山島自然保護區、佘山森林公園,綜閤得分分彆為1035.54、856.87和820.98;上海植物園相對最低,主成分得分為222.28。在此基礎上,為探明空氣負離子與各影響因子之間的關聯性,以空氣負離子為因變量,各影響因子為自變量,構建瞭上海夏季空氣負離子濃度與影響因子之間的迴歸方程,其複相關繫數為0.916,脩正多重判定繫數為0.816,可以錶徵24箇生態用地各影響因素對空氣負離子濃度關繫81.6%的信息,迴歸方程檢驗水平Sig值為0.001,迴歸關繫達極顯著水平(P<0.01),可作為上海生態用地夏季空氣負離子濃度初步預測的依據。
공기부리자불부구유살균화청길공기적공효,환능구증강심기활력,촉진신성대사,대인체건강유익。인차,위표정생태용지적건강효익,재고필식피복개도、우세물충수、협면적지수、습도、주변수역류형、전기강우사건적기출상,인입공기부리자농도지표,종합평개생태용지건강효익。이상해시24개생태용지위연구대상,2012년,선취공기질량교호적하계,대공기부리자、정리자、식피복개도、우세물충수、협면적지수、습도진행동보감측,동시결합감측점주변수역류형、감측일기적전기강우특정,이용주성분분석법(PCR)학정영향생태용지건강효익적주요성분,병대24개생태용지적건강효익진행평개。결과표명,재대생태건강효익유리적7항인자중,식피복개도재제일주성분중공헌부략차우습도,재제이주성분중공헌최대,계수분별위0.689、0.664。재24개생태용지중,해만삼림공완생태건강효익최고,기차시금산도자연보호구、사산삼림공완,종합득분분별위1035.54、856.87화820.98;상해식물완상대최저,주성분득분위222.28。재차기출상,위탐명공기부리자여각영향인자지간적관련성,이공기부리자위인변량,각영향인자위자변량,구건료상해하계공기부리자농도여영향인자지간적회귀방정,기복상관계수위0.916,수정다중판정계수위0.816,가이표정24개생태용지각영향인소대공기부리자농도관계81.6%적신식,회귀방정검험수평Sig치위0.001,회귀관계체겁현저수평(P<0.01),가작위상해생태용지하계공기부리자농도초보예측적의거。
The air negative ions not only have the effect of sterilization and air cleaning, but also can enhance the myocardial energy and promote metabolism, which were beneficial to human health. To better characterize the health benefits of ecological land, the air negative ions concentration, in addition to the vegetation coverage, dominant species, the leaf area index, humidity, type of surrounding waters, rainfall events, etc., were therefore introduced to evaluate comprehensively ecological land health benefits either. Twenty four ecological lands in shanghai were selected as research samples. In summer of 2012, the data of air ions, vegetation coverage, dominant species, the leaf area index and humidity were monitored synchronously, based on which, meanwhile taking into consideration of the surrounding waters type and precipitation characteristics, the principal component analysis (PCA) was adopted to analyze the major components that influence the health benefits, and evaluate the health benefits of the sample ecological lands. Results show that the vegetation coverage in the first group of principal components contributes only slightly less than that of humidity, but ranks the biggest one in the second group of principal components, whose Coefficients were 0.689 and 0.664, respectively. Among the twenty four ecological lands, the health benefit of Haiwan forest park was supreme, the second was the Jinshan island nature protection area, and the third was Sheshan forest park, with the average score of 1 035.54, 856.87 and 820.98 respectively;Shanghai Botanical Garden was the lowest, with the average score of 222.28. Based on the above data analysis, a regression equation was set up to formulate the interaction between air negative ions and main influencing factors, in which air negative ions was the dependent variable, and the impact factors were independent variables. Its multiple correlation coefficient was 0.916, and the corrected multiple decision coefficient was 0.816. This equation can represent 81.6%information of air negative ions and influencing factors of twenty four ecological lands. Regression equation of the inspection level sig. value was 0.001, regression relationship reached extremely significant level (P<0.01), which can be used as preliminary prediction of air negative ions concentration of ecological land of Shanghai in summer.