Modelling Temperature Trends in New Zealand

If global warming is accelerating, then one might expect temperatures for most stations to be accelerating and perhaps variability to be increasing. In this study, we examine 57 New Zealand temperature time series for evidence of non-linearity and changing variability. These correspond to time series for annual minima, annual means and annual maxima for 19 stations. Estimation is by an extended least-squares method. We find a surprising diversity of behaviour of these series – presumably reflecting their different geographic factors as well as series length. We give evidence of regions where temperatures are decreasing. For series where a linear trend is significant, it is downwards in about one third of the cases. This proportion was higher in the South Island, especially for series of minima. Where a non-linear trend is significant, temperatures are decelerating in about one half of the cases. The ratio of downward to upward trends is highest among annual maxima and South Island minima and smallest in annual means. Where a linear trend in the variability is significant, it is decreasing in 13 cases and increasing in 5 cases, although possibly this is partly due to poorer quality data last century. Where a non-linear trend in the variability is significant, variability is decelerating in about two thirds of the cases. The results are used to project upper and lower return levels of minima, means and maxima for each of the series to the year 2010.     

Meteorological normalisation and non-parametric smoothing for quality assessment and trend analysis of tropospheric ozone data

Despite extensive efforts to ensure that sampling and installation and maintenance of instruments are as efficient as possible when monitoring air pollution data, there is still an indisputable need for statistical post processing (quality assessment). We examined data on tropospheric ozone and found that meteorological normalisation can reveal (i) errors that have not been eliminated by established procedures for quality assurance and control of collected data, as well as (ii) inaccuracies that may have a detrimental effect on the results of statistical tests for temporal trends. Moreover, we observed that the quality assessment of collected data could be further strengthened by combining meteorological normalisation with non-parametric smoothing techniques for seasonal adjustment and detection of sudden shifts in level. Closer examination of apparent trends in tropospheric ozone records from EMEP (European Monitoring and Evaluation Programme) sites in Finland showed that, even if potential raw data errors were taken into account, there was strong evidence of upward trends during winter and early spring.     

Monitoring fluvial water chemistry for trend detection: hydrological variability masks trends in datasets covering fewer than 12 years

This paper sub-samples four 35 year water quality time series to consider the potential influence of short-term hydrological variability on process inference derived from short-term monitoring data. The data comprise two time series for nitrate (NO(3)-N) and two for DOC (using water colour as a surrogate). The four catchments were selected not only because of their long records, but also because the four catchments are very different: upland and lowland, agricultural and non-agricultural. Multiple linear regression is used to identify the trend and effects of rainfall and hydrological 'memory effects' over the full 35 years, and then a moving-window technique is used to subsample the series, using window widths of between 6 and 20 years. The results suggest that analyses of periods between six and eleven years are more influenced by local hydrological variability and therefore provide misleading results about long-term trends, whereas periods of longer than twelve years tend to be more representative of underlying system behaviour. This is significant: if such methods for analysing monitoring data were used to validate changes in catchment management, a monitoring period of less than 12 years might be insufficient to demonstrate change in the underlying system.     

Monitoring Air Quality in Mountains: Designing an Effective Network

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.     

Roadmap for assessing regional trends in groundwater quality

Assessing regional trends in groundwater quality can be a difficult task. Data are often scattered in space and time, and the inertia of groundwater systems can create natural, seemingly persistent changes in concentration that are difficult to separate from anthropogenic trends. Here, we show how statistical methods and software for joint analysis of multiple time series can be integrated into a roadmap for trend analysis and critical examination of data quality. Ordinary and partial Mann–Kendall (MK) tests for monotonic trends and semiparametric smoothers for multiple time series constitute the cornerstones of our procedure. The MK tests include a simple and easily implemented method to correct for serial dependence, and the associated software is designed to enable convenient handling of numerous data series and to accommodate covariates and nondetects. The semiparametric smoothers are intended to facilitate detection of synchronous changes in a network of stations. A study of Swedish groundwater quality data revealed true upward trends in acid-neutralizing capacity and downward trends in sulfate but also a misleading shift in alkalinity level that would have been difficult to detect if the time series had been analyzed separately.     

Errors in ozone risk assessment using standard conditions for converting ozone concentrations obtained by passive samplers in mountain regions

Passive samplers are often employed to measure ozone concentrations in remote areas such as mountain forests. The potential ozone risk for vegetation is then assessed by calculating the AOT40 exposure index (accumulated hourly ozone concentration exceedances above 40 ppb, i.e. AOT40 = Σ([O(3)] - 40)Δt for any hourly ozone concentration [O(3)] > 40 ppb). AOT40 is customary calculated on the basis of ozone concentrations expressed as a volumetric mixing ratio, while lab sheets normally report ozone concentrations from passive samplers in mass units per cubic metre. Concentrations are usually converted from mass units to ppb using a standard conversion factor taking SATP (Standard Ambient Temperature and Pressure) conditions into account. These conditions, however, can vary considerably with elevation. As a consequence, the blanket application of a standard conversion factor may lead to substantial errors in reporting and mapping ozone concentrations and therefore in assessing potential ozone risk in mountain regions. In this paper we carry out a sensitivity analysis of the effects of uncertainties in estimations of air temperature (T) and atmospheric pressure (P) on the concentration conversion factor, and present two examples from two monitoring and mapping exercises carried out in the Italian Alps. We derived P and T at each site from adiabatic lapse rates for temperature and pressure and analysed the magnitude of error in concentration estimations. Results show that the concentration conversion is much more sensitive to uncertainties in P gradient estimation than to air temperature errors. The concentration conversion factor (cf) deviates 5% from the standard transformation at an elevation of 500 m asl. As a consequence, the standard estimated AOT40 at this elevation is about 13% less than the actual value. AOT40 was found to be underestimated by an average between 25% and 34% at typical elevations of mountain forest stands in the Italian Alps when a correct conversion factor for transforming ozone concentrations from μg m(-3) to ppb is not applied.     

Near-surface ozone levels and trends at rural stations in France over the 1995–2003 period

There is a considerable interest in quantifying near-surface ozone concentrations and associated trends, as they serve to define the impacts on ozone of the anthropogenic precursors reductions and to evaluate the effects of emission control strategies. A statistical test has been used to the ozone air concentrations measured in the French rural monitoring network stations, called MERA, in order to bring out spatio-temporal trends in air quality in France over the 1995-2003 period. The non-parametric Mann-Kendall test has been developed for detecting and estimating monotonic trends in the time series and applied in our study at annual values: mean, 98th percentile and median based on hourly averaged ozone concentrations and applied to daily maxima. In France, when averaged overall 9 stations between 1995 and 2003, a slight increasing trend of the O(3) levels (+0.6 +/- 1.3% year( - 1)) is observed, which is strongly influenced by the concentrations of the high altitude stations. In stations below 1000 m a mean rate of -0.48% year( - 1) from annual average concentrations, of -0.45% year( - 1) for medians and of +0.56% year( - 1) for P.98 over the 1995-2003 period were obtained. In stations above 1,000 m a mean rate of +1.75% year( - 1) from annual averages values, of +4.05% year( - 1) for medians and of +2.55% year( - 1) for P.98 were calculated over the 1997-2003 period. This situation is comparable to the one observed in other countries. In Europe and in France a reduction of precursor emissions is observed whereas a slight increasing trend of the O(3) levels is observed over the 1995-2003 period. One reason is the non-linearity of chemical ozone production with respect to precursor emissions. Possible explanations are an increase in near-surface ozone values caused by a reduced ozone titration by reduced NO( x ), the meteorological parameters change, an increase in bio-geogenic compound concentrations, the intercontinental transport from North America and Asia and the influence of stratospheric-tropospheric exchanges. These possible explanations must be interpreted carefully as on the short time scales considered.     

Estimation of Ozone Trend in Central Greece, Based on Meteorologically Adjusted Time Series

Data referring to an approximately 8-year period (1999–2007) are analyzed in order to estimate the trend of the daily maximum hourly value of ozone concentration at the east coast of central Greece, where the summer background ozone concentration is high. A Kolmogorov–Zurbenko filter is applied to remove the short-term component from the raw time series of ozone and meteorological variables. Regression models are developed in order to produce meteorologically adjusted ozone time series, involving the noise-free temperature, relative humidity, and wind speed as independent variables. The analysis verifies that the meteorological adjustment provides better results on estimating ozone’s trend, which is found to be increasing (α?=?0.001) with an annual rate of 1.34?±?0.07?μg/m³. This trend could mainly be attributed to policy and changes in the emissions of ozone’s precursors. Additionally, the short-term component of ozone concentration is also meteorologically adjusted and its impact on the trend is examined. The analysis shows that its contribution is of minor importance when the ozone trend is adjusted by temperature, relative humidity, and wind speed. Moreover, the sea breeze circulation system that is frequently developed in the area influences the short-term and seasonal ozone variation, and therefore, it should be taken into account when producing meteorologically adjusted time series. The study’s conclusions could be exploited by environmental and agricultural authorities in order to develop their long-term strategies towards the air quality management.     

Statistical models for evaluating suspected artefacts in long-term environmental monitoring data

Long-term water quality monitoring is of high value for environmental management as well as for research. Artificial level shifts in time series due to method improvements, flaws in laboratory practices or changes in laboratory are a common limitation for analysis, which, however, are often ignored. Statistical estimation of such artefacts is complicated by the simultaneous existence of trends, seasonal variation and effects of other influencing factors, such as weather conditions. Here, we investigate the performance of generalised additive mixed models (GAMM) to simultaneously identify one or more artefacts associated with artificial level shifts, longitudinal effects related to temporal trends and seasonal variation, as well as to model the serial correlation structure of the data. In the same model, it is possible to estimate separate residual variances for different periods so as to identify if artefacts not only influence the mean level but also the dispersion of a series. Even with an appropriate statistical methodology, it is difficult to quantify artificial level shifts and make appropriate adjustments to the time series. The underlying temporal structure of the series is especially important. As long as there is no prominent underlying trend in the series, the shift estimates are rather stable and show less variation. If an artificial shift occurs during a slower downward or upward tendency, it is difficult to separate these two effects and shift estimates can be both biased and have large variation. In the case of a change in method or laboratory, we show that conducting the analyses with both methods in parallel strongly improves estimates of artefact effects on the time series, even if certain problems remain. Due to the difficulties of estimating artificial level shifts, posterior adjustment is problematic and can lead to time series that no longer can be used for trend analysis or other analysis based on the longitudinal structure of the series. Before carrying out a change in analytic method or laboratory, it should be considered if this is absolutely necessary. If changes cannot be avoided, the analysis of the two methods considered, or the two laboratories contracted, should be run in parallel for a considerable period of time so as to enable a good assessment of changes introduced to the data series.     

臭氧数值预报模型综述

光化学大气质量模型在研究臭氧(O_3)污染以及O_3预报方面具有核心作用,是O_3污染防治决策者的有力工具。文章结合目前中国及国际区域尺度光化学大气质量预报模型的研究与应用,重点论述与O_3有关的大气化学过程在数值预报模型中的数学表达和计算方法,阐述大气物理与大气化学过程在主流大气质量数值预报模型中的实现方法及其优势和缺陷,介绍用于数值预报模型的大气物理过程和湍流参数化方案的最新进展。就当前O_3数值模拟的主要输入资料进行讨论,强调那些易被忽视但又显著影响模型预报能力和效果的诸多因素以及模型效果评估的重要性。结合O_3与复合型大气污染的关系,强调区域大气质量数值预报模型的发展趋势与方向以及在大气环境管理方面的意义和作用。     

Robust graphical methods for diagnosing trend in irregularly spaced water quality time series

Graphical methods can play an important role in the reliable assessment of trends in typically ill behaved river quality data series both as diagnostic tools and as visual corroborative evidence when assumptions required for formal statistical tests are not met. Robust, graphically-oriented trend diagnosis procedures are presented for data series characterized by nonnormal populations, uneven time spacing, nonmonotonic trend and other factors which can create serious problems for standard parametric time series methods. Cleveland's robust locally weighted regression (RLWR) developed for investigating nonlinearity in x-y scatterplots is adapted as a robust/resistant smoothing filter for the analysis of irregular time series comprising quantitative observations. Low powered RLWR trend lines reveal temporally local phenomena, e.g. abrupt jumps (often associated with point source impacts) and periodicities, while higher powered RLWR yields smooth lines characterizing medium and longer term trends. Simple variants of Tukey smoothing concepts are developed for series with censored observations. Applications to Ontario river quality series reveal that graphical evidence is frequently sufficient to obviate the need for formal trend testing. The methods are generally applicable to most time series.     

臭氧综合指数与臭氧潜势指数的构建与应用

随着社会经济的快速发展,我国臭氧污染日益严重,因此,研发出能定量评估气象条件对臭氧污染影响程度的诊断指数,成为提高和改善气象服务质量的重要任务之一。利用中国大陆地区2018年温度、总云量、风速、风向、相对湿度等气象场数据与臭氧浓度数据,研究臭氧污染敏感气象条件,统计各气象因子分布在不同数值区间时发生臭氧污染事件的相对频率(即分指数),按照分指数最大值和最小值的差值大小进行排序,筛选出10个与臭氧污染密切相关的气象因子,将10个气象因子的分指数进行累加,即得出臭氧综合指数。随后,对各地构建臭氧综合指数时采用的气象要素进行统计,得到出现频率最高的3个气象要素,并参考这些气象要素构建了臭氧潜势指数。分别以臭氧潜势指数和臭氧综合指数对北京市2019年臭氧日最大浓度建立拟合预报模型,结果表明:两类指数的拟合预报值与实测值有着相似的变化趋势;利用臭氧综合指数计算得到的预报值与实测值的相关系数为0.76,优于利用臭氧潜势指数计算得到的预报值与实测值的相关系数(0.64)。     

区域PM2.5时空回归建模与预测

基于区域PM_(2.5)时空建模和预测的需要及PM_(2.5)浓度呈现明显的时空分布趋势的状况,以苏南地区2014年PM_(2.5)日监测数据为实验数据,使用回归克里格对区域PM_(2.5)进行时空建模和估值。利用最小二乘法建立了PM_(2.5)与时空位置的三元二次回归趋势模型,建模点趋势值与实测值间的平均误差接近于0,表明趋势模型拟合效果较好;拟合了样点残差的理论变异函数模型,表明该地区PM_(2.5)的空间和时间相关性范围分别为150 km和4 d;基于该模型,使用时空普通克里格对残差进行时空插值;插值结果与趋势项相加,得到PM_(2.5)回归克里格估值结果;通过对比不考虑趋势的时空普通克里格估值结果,发现考虑时空趋势的时空回归克里格法精度提高了1. 29%。对所提方法进行了创新性分析,并对不足之处进行了讨论。     

Minimax Statistical Models for Air Pollution Time Series. Application to Ozone Time Series Data Measured in Bordeaux

This paper deals with the application of l(infinity) (or minimax) optimization techniques to statistical modelling of high frequency air pollution data. The method was applied to ground-level ozone time-series data measured in Bordeaux over 4 years from 1998 to 2001. The aim of model building was to develop predictive models in order to provide forecasts of the maximal daily ground-level ozone concentration. Experimental results from this case study indicate that such techniques could be more appropriate than the commonly used l2 setting if only good estimation of high levels is of interest. When the free parameters are fitted by means of l(infinity) optimization techniques, the forecasting errors are more evenly distributed amongst the data points, resulting in a better estimation of high values. The paper compares the quality of forecasts produced by both a linear and a nonlinear model, using l2 and l(infinity) parameter optimization.     

京津冀区域臭氧污染趋势及时空分布特征

为研究京津冀区域的臭氧(O_3)污染情况及其时空分布特征,对2013—2015年京津冀区域13个城市80个国家环境空气监测点位的监测数据进行了统计分析。结果表明:2013—2015年,京津冀区域O_3污染状况整体呈加重趋势,其中2014年污染状况最为严重。13个城市中O_3污染最严重的城市为北京和衡水,连续3年均超标,且处于上升态势中。区域内不同城市O_3污染趋势并不相同。京津冀区域O_3浓度变化呈明显的季节变化特征,春末和夏季的O_3污染最严重。O_3-8 h(臭氧日最大8 h均值)年均值的高值区主要分布在北京中北部、承德和衡水等,2013—2015年第90百分位O_3-8 h的高值区均集中分布在北京。O_3的浓度峰值时间要晚于NOx2~5 h。O_3在春、夏季呈单峰分布,白天15:00左右出现最大值,在秋、冬季浓度较低,全天波动不大。     

Underwater noise modelling for environmental impact assessment

Assessment of underwater noise is increasingly required by regulators of development projects in marine and freshwater habitats, and noise pollution can be a constraining factor in the consenting process. Noise levels arising from the proposed activity are modelled and the potential impact on species of interest within the affected area is then evaluated. Although there is considerable uncertainty in the relationship between noise levels and impacts on aquatic species, the science underlying noise modelling is well understood. Nevertheless, many environmental impact assessments (EIAs) do not reflect best practice, and stakeholders and decision makers in the EIA process are often unfamiliar with the concepts and terminology that are integral to interpreting noise exposure predictions. In this paper, we review the process of underwater noise modelling and explore the factors affecting predictions of noise exposure. Finally, we illustrate the consequences of errors and uncertainties in noise modelling, and discuss future research needs to reduce uncertainty in noise assessments.     

应用稳健统计方法对环境空气臭氧自动监测现场比对核查结果的分析研究

根据2015年9个城市53台现场臭氧分析仪的现场比对核查结果,比较研究了稳健统计方法和一般统计方法在评价国控网臭氧自动监测数据准确性和精密性上的应用。研究表明:稳健统计能够在不剔除异常数据的前提下降低异常值对正确评价臭氧自动监测数据质量的影响,适合评价现场比对核查结果;采用Hubers方法进行稳健统计,2015年国控网臭氧日常浓度点相对偏差的95%置信区间约为-0.1%至4.5%,95%预测区间为-14.0%~18.3%,变异系数约为9.5%,数据质量仍有提升空间。     

Spatio-temporal analyses of cropland degradation in the irrigated lowlands of Uzbekistan using remote-sensing and logistic regression modeling

Advancing land degradation in the irrigated areas of Central Asia hinders sustainable development of this predominantly agricultural region. To support decisions on mitigating cropland degradation, this study combines linear trend analysis and spatial logistic regression modeling to expose a land degradation trend in the Khorezm region, Uzbekistan, and to analyze the causes. Time series of the 250-m MODIS NDVI, summed over the growing seasons of 2000–2010, were used to derive areas with an apparent negative vegetation trend; this was interpreted as an indicator of land degradation. About one third (161,000 ha) of the region’s area experienced negative trends of different magnitude. The vegetation decline was particularly evident on the low-fertility lands bordering on the natural sandy desert, suggesting that these areas should be prioritized in mitigation planning. The results of logistic modeling indicate that the spatial pattern of the observed trend is mainly associated with the level of the groundwater table (odds?=?330 %), land-use intensity (odds?=?103 %), low soil quality (odds?=?49 %), slope (odds?=?29 %), and salinity of the groundwater (odds?=?26 %). Areas, threatened by land degradation, were mapped by fitting the estimated model parameters to available data. The elaborated approach, combining remote-sensing and GIS, can form the basis for developing a common tool for monitoring land degradation trends in irrigated croplands of Central Asia.