Philipp Schneider

Rapid and highly variable warming of lake surface waters around the globe

In this first worldwide synthesis of in situ and satellite-derived lake data, we find that lake summer surface water temperatures rose rapidly (global mean = 0.34°C decade−1) between 1985 and 2009. Our analyses show that surface water warming rates are dependent on combinations of climate and local characteristics, rather than just lake location, leading to the counterintuitive result that regional consistency in lake warming is the exception, rather than the rule. The most rapidly warming lakes are widely geographically distributed, and their warming is associated with interactions among different climatic factors—from seasonally ice-covered lakes in areas where temperature and solar radiation are increasing while cloud cover is diminishing (0.72°C decade−1) to ice-free lakes experiencing increases in air temperature and solar radiation (0.53°C decade−1). The pervasive and rapid warming observed here signals the urgent need to incorporate climate impacts into vulnerability assessments and adaptation efforts for lakes.

A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009

Global environmental change has influenced lake surface temperatures, a key driver of ecosystem structure and function. Recent studies have suggested significant warming of water temperatures in individual lakes across many different regions around the world. However, the spatial and temporal coherence associated with the magnitude of these trends remains unclear. Thus, a global data set of water temperature is required to understand and synthesize global, long-term trends in surface water temperatures of inland bodies of water. We assembled a database of summer lake surface temperatures for 291 lakes collected in situ and/or by satellites for the period 1985–2009. In addition, corresponding climatic drivers (air temperatures, solar radiation, and cloud cover) and geomorphometric characteristics (latitude, longitude, elevation, lake surface area, maximum depth, mean depth, and volume) that influence lake surface temperatures were compiled for each lake. This unique dataset offers an invaluable baseline perspective on global-scale lake thermal conditions as environmental change continues.

Can commercial low-cost sensor platforms contribute to air quality monitoring and exposure estimates?

The emergence of low-cost, user-friendly and very compact air pollution platforms enable observations at high spatial resolution in near-real-time and provide new opportunities to simultaneously enhance existing monitoring systems, as well as engage citizens in active environmental monitoring. This provides a whole new set of capabilities in the assessment of human exposure to air pollution. However, the data generated by these platforms are often of questionable quality. We have conducted an exhaustive evaluation of 24 identical units of a commercial low-cost sensor platform against CEN (European Standardization Organization) reference analyzers, evaluating their measurement capability over time and a range of environmental conditions. Our results show that their performance varies spatially and temporally, as it depends on the atmospheric composition and the meteorological conditions. Our results show that the performance varies from unit to unit, which makes it necessary to examine the data quality of each node before its use. In general, guidance is lacking on how to test such sensor nodes and ensure adequate performance prior to marketing these platforms. We have implemented and tested diverse metrics in order to assess if the sensor can be employed for applications that require high accuracy (i.e., to meet the Data Quality Objectives defined in air quality legislation, epidemiological studies) or lower accuracy (i.e., to represent the pollution level on a coarse scale, for purposes such as awareness raising). Data quality is a pertinent concern, especially in citizen science applications, where citizens are collecting and interpreting the data. In general, while low-cost platforms present low accuracy for regulatory or health purposes they can provide relative and aggregated information about the observed air quality.

Data assimilation: making sense of Earth Observation

Climate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior knowledge, the latter typically embodied in a model describing relationships between variables of the Earth System. Data assimilation provides an objective methodology to combine observational and model information to provide an estimate of the most likely state and its uncertainty for the whole Earth System. This approach adds value to the observations – by filling in the spatio-temporal gaps in observations; and to the model – by constraining it with the observations. In this review paper we motivate data assimilation as a methodology to fill in the gaps in observational information; illustrate the data assimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface); and discuss the outlook for data assimilation, including the novel application of data assimilation ideas to observational information obtained using Citizen Science. Ultimately, a strong motivation of data assimilation is the many benefits it provides to users. These include: providing the initial state for weather and air quality forecasts; providing analyses and reanalyses for studying the Earth System; evaluating observations, instruments and models; assessing the relative value of elements of the Global Observing System (GOS); and assessing the added value of future additions to the GOS.

Mapping urban air quality in near real-time using observations from low-cost sensors and model information

The recent emergence of low-cost microsensors measuring various air pollutants has significant potential for carrying out high-resolution mapping of air quality in the urban environment. However, the data obtained by such sensors are generally less reliable than that from standard equipment and they are subject to significant data gaps in both space and time. In order to overcome this issue, we present here a data fusion method based on geostatistics that allows for merging observations of air quality from a network of low-cost sensors with spatial information from an urban-scale air quality model. The performance of the methodology is evaluated for nitrogen dioxide in Oslo, Norway, using both simulated datasets and real-world measurements from a low-cost sensor network for January 2016. The results indicate that the method is capable of producing realistic hourly concentration fields of urban nitrogen dioxide that inherit the spatial patterns from the model and adjust the prior values using the information from the sensor network. The accuracy of the data fusion method is dependent on various factors including the total number of observations, their spatial distribution, their uncertainty (both in terms of systematic biases and random errors), as well as the ability of the model to provide realistic spatial patterns of urban air pollution. A validation against official data from air quality monitoring stations equipped with reference instrumentation indicates that the data fusion method is capable of reproducing city-wide averaged official values with an R2 of 0.89 and a root mean squared error of 14.3 μg m-3. It is further capable of reproducing the typical daily cycles of nitrogen dioxide. Overall, the results indicate that the method provides a robust way of extracting useful information from uncertain sensor data using only a time-invariant model dataset and the knowledge contained within an entire sensor network.

Localized real-time information on outdoor air quality at kindergartens in Oslo, Norway using low-cost sensor nodes

Abstract In Norway, children in kindergartens spend significant time outdoors under all weather conditions, and there is thus a natural concern about the quality of outdoor air. It is well known that air pollution is associated with a wide variety of adverse health impacts for children, with greater impact on children with asthma. Especially during winter and spring, kindergartens in Oslo that are situated close to streets with busy traffic, or in areas where wood burning is used for house heating, can experience many days with bad air quality. During these periods, updated information on air quality levels can help the kindergarten teachers to plan appropriate outdoor activities and thus protect childrens health. We have installed 17 low‐cost air quality nodes in kindergartens in Oslo. These nodes are smaller, cheaper and less complex to use than traditional equipment. Performance evaluation shows that while they are less accurate and suffer from higher uncertainty than reference equipment, they still can provide reliable coarse information about local pollution. The main challenge when using this technology is that calibration parameters might change with time depending on the atmospheric conditions. Thus, even if the sensors are calibrated a priori, once deployed, and especially if they are deployed for a long time, it is not possible to determine if a node is over‐ or under‐estimating the concentration levels. To enhance the data from the sensors, we employed a data fusion technique that allows generating a detailed air quality map merging the data from the sensors and the data from an urban model, thus being able to offer air quality information to any location within Oslo. We arranged a focus group with the participation of local administration, kindergarten staff and parents to understand their opinion and needs related to the air quality information that was provided to the participant kindergartens. They expressed concern about the data quality but agree that having updated information on the air quality in the surroundings of kindergartens can help them to reduce childrens exposure to air pollution. HighlightsWe show that low‐cost sensors can provide an indication of outdoor air quality.We employed a data fusion method to provide real‐time air quality maps.Localized real‐time air quality information can help parents and kindergarten staff.

A Network of Low-Cost Air Quality Sensors and Its Use for Mapping Urban Air Quality

Recent rapid technological advances in sensor technology have resulted in a wide variety of small and low-cost microsensors with significant potential for measuring air pollutants. In this contribution, we evaluate the performance of a commercially available low-cost sensor platform for air quality and show how the data from a network of such devices can be used for high-resolution mapping of urban air quality. Our results indicate that the sensor platforms are subject to a significant sensor-to-sensor variability as well as strong dependencies on environmental conditions. A field calibration of all individual sensor devices by co-locating them with an air quality monitoring station equipped with reference instrumentation is thus required for obtaining the best possible results. We further demonstrate that, despite relatively low accuracy at the individual sensor level, a methodology based on geostatistical data fusion is capable of merging the information from the sensor network with model information in such a way that we can obtain realistic and frequently updated maps of urban air quality. We show that exploiting the “swarm knowledge” of the entire network of sensors is capable of extracting useful information from the data even though individual sensors are subject to significant uncertainty.

Global trends in lake temperatures observed from space

This study uses the existing archive of spaceborne thermal infrared imagery to generate multi-decadal time series of lake surface temperature for 169 of the largest inland water bodies worldwide, and to estimate trends for the water bodies. The results indicate that the nighttime summertime/dry-season surface temperatures of the studied water bodies have been increasing with an average rate of 0.045 ± 0.011 °C yr-1 for the period 1985 to 2009. Individual lakes have shown warming rates as high as 0.13 ± 0.01 °C yr-1. On the global scale, the data show the greatest warming in the mid- and high latitudes of the Northern hemisphere, and particularly in Northern Europe with spatially consistent rates of approximately 0.08 ± 0.01 °C yr-1. The results of this study provide a critical new independent data source on studying the effects of climate change.

Global trends in tropospheric NO 2 observed from space

Here we present results of a global trend analysis using nearly a decade of NO2 observations acquired by the SCIA-MACHY (SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) instrument onboard the Envisat satellite platform. Monthly average tropospheric NO2 column data was acquired for the period between August 2002 and August 2011. A trend analysis was subsequently performed by fitting a statistical model including a seasonal cycle and linear trend to the time series extracted at each grid cell. The linear trend component and the trend uncertainty were then mapped spatially at both regional and global scales. The results show that spatially contiguous areas of significantly increasing NO2 levels are found primarily in Eastern China. In addition, many urban agglomerations in Asia and the Middle East similarly exhibit significantly increasing trends, with Dhaka in Bangladesh being the megacity with the most rapid relative increase during the study period. In contrast, significantly decreasing trends in NO2 levels exist over large parts of Europe and the Eastern United States. The satellite-derived time series were further analysed with respect to identification of the impact of the 2008/2009 economic crisis. European trends obtained from the satellite analysis are also compared with corresponding trends computed using data of the EMEP model, as well as with NO2 trends calculated from station observations throughout Europe.