Spatial analysis of extreme precipitation in Sweden 1961-2000

Extreme daily precipitation in Sweden for the years 1961-2000 is analyzed with respect to spatial scale, regional variations and associated weather types. Correlograms based on a lag distance of 30 km estimated the spatial scale of variation of the annual mean precipitation, the 99th percentile of daily precipitation and the average of annual maximum daily precipitation to 100 km, 60-100 km and 40-70 km, respectively. Regions of correlation with respect to precipitation at 82 stations during days of extreme events are identified through Maximum-Likelihood Factor Analysis. Eleven factors are found to provide the optimum factor solution. Weather types for the days of extreme events are determined by an objective classification scheme, based on daily sea level pressure, which is modified by subjective inclusion of fronts. In total, 63% of the extreme events occurred during cyclonic weather types, 32% during frontal, 3% during directional and 2% during anticyclonic types. The frequency of the weather types during extreme events varied between the regions however.     

Regulating waste discharge with fuzzy interval effluent limitations for toxic pollutants.

Many traditional water quality standards are based on extreme percentiles; often, there is the risk of making wrong decisions with these standards because of high estimation uncertainty. Standards expressed as fuzzy intervals in the form of [trigger, enforcement limitation] make it possible to control the risks for the discharger and the consumer simultaneously. With fuzzy interval compliance, corrective action is initiated when the trigger is exceeded; noncompliance is declared when the enforcement limitation is exceeded. Fuzzy intervals would digest the risks that are inherent when a single enforcement limitation is used to determine compliance; the risks can be further lowered when the fuzzy intervals are based on less extreme percentiles. This paper proposes several alternatives to using a single extreme percentile standard for regulating water quality or waste discharge. A case study using municipal effluent water quality data was included that suggests methods to determine compliance with fuzzy interval standards.     

Ecosystem Response to Climatic Change: The Importance of the Cold Season

Winter climate and snow cover are the important drivers of plant community development in polar regions. However, the impacts of changing winter climate and associated changes in snow regime have received much less attention than changes during summer. Here, we synthesize the results from studies on the impacts of extreme winter weather events on polar heathland and lichen communities. Dwarf shrubs, mosses and soil arthropods were negatively impacted by extreme warming events while lichens showed variable responses to changes in extreme winter weather events. Snow mould formation underneath the snow may contribute to spatial heterogeneity in plant growth, arthropod communities and carbon cycling. Winter snow cover and depth will drive the reported impacts of winter climate change and add to spatial patterns in vegetation heterogeneity. The challenges ahead lie in obtaining better predictions on the snow patterns across the landscape and how these will be altered due to winter climate change.     

Ecosystem response to climatic change: the importance of the cold season

Winter climate and snow cover are the important drivers of plant community development in polar regions. However, the impacts of changing winter climate and associated changes in snow regime have received much less attention than changes during summer. Here, we synthesize the results from studies on the impacts of extreme winter weather events on polar heathland and lichen communities. Dwarf shrubs, mosses and soil arthropods were negatively impacted by extreme warming events while lichens showed variable responses to changes in extreme winter weather events. Snow mould formation underneath the snow may contribute to spatial heterogeneity in plant growth, arthropod communities and carbon cycling. Winter snow cover and depth will drive the reported impacts of winter climate change and add to spatial patterns in vegetation heterogeneity. The challenges ahead lie in obtaining better predictions on the snow patterns across the landscape and how these will be altered due to winter climate change.     

Changes in extreme events and the potential impacts on human health

Extreme weather and climate-related events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, dust storms, flooding rains, coastal flooding, storm surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden. More information is needed about the impacts of climate change on public health and economies to effectively plan for and adapt to climate change. This paper describes some of the ways extreme events are changing and provides examples of the potential impacts on human health and infrastructure. It also identifies key research gaps to be addressed to improve the resilience of public health to extreme events in the future.

Implications: Extreme weather and climate events affect human health by causing death, injury, and illness, as well as having large socioeconomic impacts. Climate change has caused changes in extreme event frequency, intensity, and geographic distribution, and will continue to be a driver for change in the future. Some of these events include heat waves, droughts, wildfires, flooding rains, coastal flooding, surges, and hurricanes. The pathways connecting extreme events to health outcomes and economic losses can be diverse and complex. The difficulty in predicting these relationships comes from the local societal and environmental factors that affect disease burden.     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.     

Assessment of the probability of extreme weather events and their potential effects in large conurbations

The likelihood of occurrence of extreme high-temperature run events is estimated for different values of the event intensity and persistence from very long synthetic time series of daily maximum temperatures generated by Monte Carlo simulations using a first-order autoregressive or Markov model. A theoretical analysis reveals a higher relative sensitivity of the simulated extreme event probabilities to changes in the variability of climate than to changes in its mean state. Moreover, this sensitivity relatively increases at a nonlinear rate the more extreme the event. The developed probabilistic model is applied in order to derive local scenarios of extreme high-temperature run events for a large conurbation like the city of Berlin assuming both arbitrary hypothetical and physically based new climate states described by changes in the model parameters (e.g. the mean, the standard deviation and the first-order autocorrelation of the daily maximum temperature time series). As a consequence of a 1.7°C increase in the mean as well as a 19% increase in the temperature variability in July as predicted by the climate model ECHAM_1/LSG assuming an unrestricted future increase in the global atmospheric concentration of climate relevant greenhouse gases according to the IPCC Scenario A (“Business as usual”) the intensity as well as the persistence of extreme high-temperature run events will rise considerably up to the end of the next century. In particular, intense hot spells characterized by at least five consecutive daily maximum temperatures equaling or exceeding 33°C are expected to occur every eight years under the new climate conditions compared to a current repetition time of about 47 years. The potential environmental effects might be a significant increase in the heat-stress-related morbidity and mortality rate, an aggravation of the summer smog situation and a destabilization of the urban ecosystems.     

Long range transport of fine particle windblown soils and coal fired power station emissions into Hanoi between 2001 to 2008

Fine particulate matter (PM_2.5), source fingerprints and their contributions have been measured and reported previously at Hanoi, Vietnam, from 25 April 2001 to 31 December 2008. In this study back trajectories are used to identify long range transport into Hanoi for two of these sources, namely, windblown dust (Soil) from 12 major deserts in China and emissions from 33 coal fired power plants (Coal) in Vietnam and China. There were 28 days of extreme Soil events with concentrations greater than 6 μg m^?3 and 25 days of extreme Coal with concentrations greater than 30 μg m^?3 from a total of 748 sampling days during the study period. Through the use of back trajectories it was found that long range transport of soil from the Taklamakan and Gobi desert regions (more than 3000 km to the north west) accounted for 76% of the extreme events for Soil. The three local Vietnamese power stations contributed to 15% of the extreme Coal events, while four Chinese power stations between 300 km and 1700 km to the north-east of Hanoi contributed 50% of the total extreme Coal events measured at the Hanoi sampling site.     

A review of statistical methods for the meteorological adjustment of tropospheric ozone

A variety of statistical methods for meteorological adjustment of ozone have been proposed in the literature over the last decade for purposes of forecasting, estimating ozone time trends, or investigating underlying mechanisms from an empirical perspective. The methods can be broadly classified into regression, extreme value, and space–time methods. We present a critical review of these methods, beginning with a summary of what meteorological and ozone monitoring data have been considered and how they have been used for statistical analysis. We give particular attention to the question of trend estimation, and compare selected methods in an application to ozone time series from the Chicago area. We conclude that a number of approaches make useful contributions to the field, but that no one method is most appropriate for all purposes and all meteorological scenarios. Methodological issues such as the need for regional-scale analysis, the nonlinear dependence of ozone on meteorology, and extreme value analysis for trends are addressed. A comprehensive and reliable methodology for space–time extreme value analysis is attractive but lacking.     

Long-term effects on nitrogen and benthic fauna of extreme weather events: Examples from two Swedish headwater streams

Climate change is expected to cause an increased frequency of extreme events such as heavy floods and major storms. Such stochastic events have an immediate impact on surface water quality, but the long-term effects are largely unknown. In this study, we assess long-term monitoring data from two Swedish headwater catchments affected by extreme weather events. At one site, where nitrogen effects in soil water, groundwater, and stream water were studied after storm-felling and subsequent forest dieback from bark beetle attack, long-term (>5 years) but relatively modest (generally <1 mg L^?1) increases in ammonium (NH₄-N) and nitrate (NO₃-N) concentrations were observed in the various aqueous media. At the other site, where effects on benthic fauna were studied in a stream impacted by extreme geophysical disturbances caused by rainstorm-induced flashflood, only short-term (1 year) effects were revealed both regarding diversity and composition of species.     

Moving beyond traditional macrofaunal community structure studies in the Indian Ocean continental shelf: a research synthesis based on research weaving

Environmental Science and Pollution Research - The Indian Ocean (IO) continental shelf characterized by unique oceanographic and meteorological features and extreme habitat is a biodiversity...     

Main and added effects of heatwaves on hospitalizations for mental and behavioral disorders in a tropical megacity of Vietnam

Environmental Science and Pollution Research - Vietnam is highly vulnerable to climate change-related extreme weather events such as heatwaves. This study assesses the association between heatwaves...     

Impact of ambient temperature on hospital admissions for respiratory diseases in suburban rural villages in northwest China

Environmental Science and Pollution Research - Extreme temperature is strongly associated with human health, but few studies are available for the effects of extreme temperatures on respiratory...     

Emergence of nano silica for oil and gas well cementing: application,challenges, and future scope

Environmental Science and Pollution Research - Nanotechnology has opened up a plethora of opportunities and has acquired extreme importance in a myriad of fields to produce enhanced materials....     

Economic feasibility of solar-powered reverse osmosis water desalination: a comparative systemic review

Environmental Science and Pollution Research - Due to disparities in the allocation of rainwater and drought, extreme exploitation of groundwater reservoirs has depleted water supplies in many...     

Macrobenthic invertebrates as bioindicators of trace elements in high-mountain lakes

Environmental Science and Pollution Research - Alpine lakes are extreme ecosystems located in remote areas and populated by few but well-adapted species. Because of their remote location, they are...     

A review of the impact of outdoor and indoor environmental factors on human health in China

Environmental Science and Pollution Research - The Intergovernmental Panel on Climate Change (IPCC) reported that global climate change has led to the increased occurrence of extreme weather...     

The impact of COVID-19 pandemic on air pollution: a global research framework,challenges, and future perspectives

Environmental Science and Pollution Research - As a result of extreme modifications in human activity during the COVID-19 pandemic, the status of air quality has recently been improved. This...     

Release of particles and metals into seawater following sediment resuspension of a coastal mine tailings disposal off Portmán Bay,Southern Spain

Environmental Science and Pollution Research - Portmán Bay in Southern Spain is one of the most extreme cases in Europe of anthropogenic impact on the marine ecosystem by the disposal of mine...     

Comparative toxicity assessment of in situ burn residues to initial and dispersed heavy fuel oil using zebrafish embryos as test organisms

Environmental Science and Pollution Research - In situ burning (ISB) is discussed to be one of the most suitable response strategies to combat oil spills in extreme conditions. After burning, a...