Long-term trends of US county-level extreme ozone concentrations with long memory and changepoint considerations
研究了美国395个县每周最大地面臭氧浓度的长期线性趋势，通过考虑变点和长记忆自相关，发现调整变点后45.82%的县极端臭氧趋势上升，对政策评估和环境研究有参考价值。
Abstract We quantify long-term linear trends in weekly maximum ground-level ozone concentrations of 395 US counties by considering two critical issues: changepoints and long-memory autocorrelation. First, due to changes in air quality policy and regulation, monitor location, instrument, sampling protocols, etc., ozone data often exhibit inhomogeneities that, if ignored, could result in erroneous estimation of long-term trends. Second, many county-level weekly maximum ozone series exhibit long-memory autocorrelation. Changepoint methods that do not consider long-memory autocorrelation risk detecting spurious changepoints. For accurate estimation of long-term trends, we develop a long-memory extreme series model using a copula transformation. This model then incorporates a genetic algorithm method to estimate the number and times of changepoints in the ozone series. With the estimated changepoints used in our long-memory extreme series model, we find that of those counties with at least one changepoint, 27.56% have their long-term trend estimates changed by more than 0.03 ppm century−1. We also find that although overall levels of extreme ozone have been reduced, partially due to air quality policies, extreme ozone trends have increased in 45.82% of the counties when adjusted for changepoint-inducing events. Spatial patterns of our long-term trend estimates and 10-year return levels are summarized in maps.