PM2.5 in China: Cycles, Trend, and Regional Correlation Shitian Liu.

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Presentation transcript:

PM2.5 in China: Cycles, Trend, and Regional Correlation Shitian Liu

Background on Geography Five major cities where the US Embassies are located.

How to find the threshold for “extreme value” ? % = % = Does not include NAN days.

Beijing Monthly Average Peaks in Winter Possibly due to low precipitation Not due to heating because Guangzhou and Shanghai do not have indoor heating but still show the peaks Valleys in Summer Possibly due to higher precipitation Jan 2011 is suspicious!

Cycles Can definitely see yearly cycles. But no weekly cycle!

Trend Daily Average SlopeConfiden ce Interv al Least- Square Bootstrap NO TREND!

Trend Monthly Average SlopeConfidenc e Interval Least-Square e-04 Bootstrap NO TREND!

Extreme Days SlopeConfidence Interval [ ,0.0220] NO TREND!

Slope Confidenc eInterval Extreme Values NO TREND!

Extreme Values Slope Confidenc eInterval NO TREND!

Correlation Coefficient BeijingShanghai Guangzho u Beijing Shanghai Guangzho u BeijingShanghai Guangzho u Beijing E-08 Shanghai E-07 Guangzho u7.21E E-071 Correlation Coefficients P values Although p-values for the correlation coefficients between Guangzhou & Beijing and Guangzhou & Shanghai is less than 0.05, the correlations are weak. WEAK CORRELATION!

Cross Correlation The Correlations between the three cities are weak, which implies that PM2.5 is possibly a local pollutant instead of a regional pollutant. Again….

Conclusions PM2.5 concentration in all four cities show yearly cycle, but not weekly cycle. PM2.5 concentration shows no significant trend in China’s major cities. No significant decrease in number of extreme days, neither. PM2.5 concentration shows no regional correlation which implies that the pollutant is local.

References StateAir: U.S. Department of State Air Quality Monitoring Program