Abstract
Spontaneous pneumothoraces (SP) tend to cluster. Correlations between SP and atmospheric variations were reported by previous studies. In our work SP correlation with meteo variables and air pollutants in Cuneo County was analyzed.
2004-2010, 451 SP patients were prospectively evaluated. For each day of analyzed period, meteo parameters and pollutants were recorded. Statistics on SP evaluated distribution characteristics, spectral autocorrelation and spectral analysis; multivariate regression techniques were performed using artificial neural networks.
Analysis of seasonal distributions showed no significant correlation. Spectral analysis showed that SP events were not random. Correlations between meteo-environmental variables were analyzed through linear tests.

Linear tests on meteo variables and pollutants
VariablestKolmogorov-SmirnovMann-Whitney
Temperature (T)0.0030.0730.037
Humidity (H)0.0460.0150.089
Pressure (P)0.0900.0340.083
Wind (W)0.0370.4150.070
NO20.0220.1650.050
O30.0270.0920.044


Neural networks showed some variables may predict SP insurgence.

Multivariate regression
O3+NO2+W+P+TrsFARFPFNDPEFHSS
Neural network0.150.750.520.300.700.340.11
Regression0.040.780.320.640.360.250.03
NO2+W+P+T
Neural network0.130.740.390.450.550.340.12
Regression0.010.790.270.790.280.210.01
O3+NO2+W+P
Neural network0.180.730.480.300.700.360.14
Regression0.010.800.620.380.630.240.01
rs: correlation. Best performance: ­Heidke‘s Skill Statistics (HSS); ¯false alarm ratio (FAR)+false negative (FN); ­detection probability (DP)+efficiency (EF)


SP occurrence significantly increases in warm windy days with high atmospheric pressure and high NO2 concentration. These data don‘t affect SP treatment; nevertheless, they add information on SP tendency to cluster.