Mercury (Hg) researchers have made progress in understanding atmospheric Hg, especially with respect to oxidized Hg (HgII) that can represent 2 to 20% of Hg in the atmosphere. Knowledge developed over the past ∼10 years has pointed to existing challenges with current methods for measuring atmospheric Hg concentrations and the chemical composition of HgII compounds. Because of these challenges, atmospheric Hg experts met to discuss limitations of current methods and paths to overcome them considering ongoing research. Major conclusions included that current methods to measure gaseous oxidized and particulate-bound Hg have limitations, and new methods need to be developed to make these measurements more accurate. Developing analytical methods for measurement of HgII chemistry is challenging. While the ultimate goal is the development of ultrasensitive methods for online detection of HgII directly from ambient air, in the meantime, new surfaces are needed on which HgII can be quantitatively collected and from which it can be reversibly desorbed to determine HgII chemistry. Discussion and identification of current limitations, described here, provide a basis for paths forward. Since the atmosphere is the means by which Hg is globally distributed, accurately calibrated measurements are critical to understanding the Hg biogeochemical cycle.

Quality control of large-scale monitoring networks requires the use of automatic procedures to detect potential outliers in an unambiguous and reproducible manner. This paper describes a methodology that combines existing statistical methods to accommodate for the specific characteristics of measurement data obtained from groundwater quality monitoring networks: the measurement series show a large variety of dynamics and often comprise few (< 25) measurements, the measurement data are not normally distributed, measurement series may contain several outliers, there may be trends in the series, and/or some measurements may be below detection limits. Furthermore, the detection limits may vary in time. The methodology for outlier detection described in this paper uses robust regression on order statistics (ROS) to deal with measured values below the detection limit. In addition, a biweight location estimator is applied to filter out any temporal trends from the series. The subsequent outlier detection is done in z-score space. Tuning parameters are used to attune the robustness and accuracy to the given dataset and the user requirements. The method has been applied to data from the Dutch national groundwater quality monitoring network, which consists of approximately 350 monitoring wells. It proved to work well in general, detecting outliers at the top and bottom of the regular measurement range and around the detection limit. Given the diversity exhibited by measurement series, it is to be expected that the method does not give 100% satisfactory results. Measured values identified by the method as potential outliers will therefore always need to be further assessed on the basis of expert knowledge, consistency with other measurement data and/or additional research.

Saltwater intrusion (SWI) into coastal aquifers is a growing problem for the drinking water supply of coastal communities worldwide, including for the sustainability of coastal ecosystems depending on freshwater inflow. The interface between freshwater and seawater in coastal aquifers is highly dynamic and is sensitive to changes in the hydraulic gradient between the sea- and groundwater levels. Sea level rise, storm surges, and drought are natural drivers changing the hydrostatic equilibrium between fresh- and saltwater. Coastal aquifers are further stressed by groundwater over-pumping because of the increasing needs of coastal populations. A systematic literature review and analysis of the current state of understanding the SWI drivers is presented, focusing on recent (1980 to 2020) investigations in the contiguous United States (CONUS). Results confirm that SWI is an active research area in CONUS. The drivers of SWI are increasingly better understood and quantified; however, the need for increased monitoring is also recognized. Our study shows that the number of monitoring sites have not increased significantly over the review period. Additionally, geophysical, and geochemical investigation techniques and numerical modeling tools are not utilized to their full potential, and data on SWI is not readily available from some sources. We conclude that there is a need for more SWI monitoring networks and closer multi-disciplinary collaboration, particularly between practitioners in the field and emerging modeling technique experts. Though we focus primarily on CONUS, our insights may be of value to the broader SWI research community and coastal water quality managers around the globe.

The importance of selecting appropriate air pollution monitoring sites in a city is vital for accurately reporting air quality, enhancing the quality of high-resolution modelling and informing policy to implement measures to deliver cleaner air in the urban environment. COVID-19 restrictions impacted air quality in urban centres worldwide as reduced mobility led to changes in traffic-related air pollution (TRAP). As such, it offered a unique dataset to examine the spatial and temporal variations in air quality between monitoring stations in Dublin, Ireland. Firstly, an analysis of mobility data showed reductions across almost all sectors after COVID-19 restrictions came into place, which was expected to lower TRAP. In addition, similar changes in air quality were evident to other cities around the world: reductions in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations and an increase in ozone (O3) concentrations. Average daily and diurnal concentrations for these three pollutants presented more statistically significant spatial and temporal changes during COVID-19 restrictions at monitoring sites with urban or traffic classifications than suburban background sites. Furthermore, substantial reductions in the range of average hourly pollutant concentrations were observed, 79% for PM2.5 and 75% for NO2, with a modest 24% reduction for O3. Correlation analysis of air pollution between monitoring sites and years demonstrated an improvement in the R2 for NO2 concentrations only, suggesting that spatiotemporal homogeneity was most notable for this TRAP due to mobility restrictions during COVID-19. The spatiotemporal representativeness of monitoring stations across the city will change with greener transport, and air quality during COVID-19 can provide a benchmark to support the introduction of new policies for cleaner air.

Waste management plants are one of the most important sources of odorants that may cause odor nuisance. The monitoring of processes involved in the waste treatment and disposal as well as the assessment of odor impact in the vicinity of this type of facilities require two different but complementary approaches: analytical and sensory. The purpose of this work is to present these two approaches. Among sensory techniques dynamic and field olfactometry are considered, whereas analytical methodologies are represented by gas chromatography-mass spectrometry (GC-MS), single gas sensors and electronic noses (EN). The latter are the core of this paper and are discussed in details. Since the design of multi-sensor arrays and the development of machine learning algorithms are the most challenging parts of the EN construction a special attention is given to the recent advancements in the sensitive layers development and current challenges in data processing. The review takes also into account relatively new EN systems based on mass spectrometry and flash gas chromatography technologies. Numerous examples of applications of the EN devices to the sensory and analytical measurements in the waste management plants are given in order to summarize efforts of scientists on development of these instruments for constant monitoring of chosen waste treatment processes (composting, anaerobic digestion, biofiltration) and assessment of odor nuisance associated with these facilities.

Atmospheric nitrogen (N) deposition is a vital component of the global N cycle. Excessive N deposition on the Earth\'s surface has adverse impacts on ecosystems and humans. Quantification of atmospheric N deposition is indispensable for assessing and addressing N deposition-induced environmental issues. In the present review, we firstly summarized the current methods applied to quantify N deposition (wet, dry, and total N deposition), their advantages and major limitations. Secondly, we illustrated the long-term N deposition monitoring networks worldwide and the results attained via such long-term monitoring. Results show that China faces heavier N deposition than the United States, European countries, and other countries in East Asia. Next, we proposed a framework for estimating the atmospheric wet and dry N deposition using a combined method of surface monitoring, modeling, and satellite remote sensing. Finally, we put forth the critical research challenges and future directions of the atmospheric N deposition. CAPSULE: A review of quantification methods and the global data on nitrogen deposition and a systematic framework was proposed for quantifying nitrogen deposition.

The monitoring of metals in ambient air has been undertaken for over 40 years on a national basis in the UK. During this period, the UK pollution landscape has continued to evolve in terms of emission sources, and the measurement framework for metals in ambient air, the UK Heavy Metals Monitoring Network, has also been subject to significant configuration changes. Therefore, this work provides a timely review of more recent concentration trends in the context of current emission profiles. Overall, throughout this time period, there has been a significant downward trend in the emissions and consequently, the measured concentrations of most metals in UK ambient air. Ambient concentrations were generally found to be well correlated with emission estimates. Analysis of the sensitivity of measured concentrations to emissions suggests that concentrations have fallen faster than the reduction in emission estimates would have predicted at typical median urban background sites.

The increasing expansion of the cities together with activities carried out on the environment by men have contributed to the deterioration of air quality. Air quality index measures how much air is free from pollution. Being aware of the healthiness of the breathed air is a right for the people. Public authorities must constantly inform the population on air quality status. Even though several pollutants are monitored by the air quality monitoring networks, providing a significant amount of data, their interpretation and presentation to the population by the public authorities is a difficult task. Some countries, for several years, have adopted evaluation procedures through daily indices that succinctly describe the air quality status in different areas of the city. The use of an index which is able to give a simple and quick information to the population represents a possible solution for the public authorities. Concerning a Mediterranean area, it has not yet been possible to adopt a single indicator to be used for informing the population. In this work, the air quality status is highlighted by the air quality index (AQI) whose purpose is to inform, in a simple and immediate way, the population. Analyzing the AQI\'s trend from 2013 to 2015, it was possible to assess the air quality status, obtaining an overall scenario for the purpose of protecting human health and the ecosystems. We point out that this kind of research could be applied to every region or municipality.

Rivers extend in space and time under the influence of their catchment area. Our perception largely relies on discrete spatial and temporal observations carried out at certain sites located throughout the catchment (monitoring networks, MN). However, MNs are constrained by (a) the distribution of sampling sites, (b) the dynamics of the variable considered and (c) the river hydrological conditions. In this study, all three aspects were captured and quantified by applying a spatial autocorrelation modeling approach. We exemplarily studied its application to 235 emerging contaminants (pesticides, pharmaceuticals, and personal care products [PPCP], industrial and miscellaneous) measured at 55 sampling sites in the Danube River. 22 out of the 235 compounds monitored were present at all sites and 125 were found in at least 50%.We first calculated the Moran Index (MI) to characterize the spatial autocorrelation of the compound set. 59 compounds showed MI≤0, which can be interpreted as \'no spatial correlation\'. Next, spatial autocorrelation models were set for each compound. From the autocorrelation parameter ρ, catchment average correlation lengths were derived for each compound. MN optimality was examined and compounds were classified into three groups: (a) those with ρ≤0 [25%]; (b) those with ρ>0 and correl. length0 and correl. length>average distance between consecutive sites [73%]. The MN was considered optimal only for the latter class. Networks with the larger average distance between consecutive sites resulted in a decreasing number of optimally monitored compounds. Furthermore, neighbors vs. local relative contributions were quantified based on the spatial autocorrelation model for all the measured compounds. The results of this study show how autocorrelation models can aid water managers to improve the design of river MNs, which are a key aspect of the Water Framework Directive.

Ozone (O3) is a photochemically formed reactive gas responsible for a decreasing carbon assimilation in plant ecosystems. Present in the atmosphere in trace concentrations (less than 100 ppbv), this molecule is capable of inhibiting carbon assimilation in agricultural and forest ecosystems. Ozone-risk assessments are typically based on manipulative experiments. Present regulations regarding critical ozone levels are mostly based on an estimated accumulated exposure over a given threshold concentration. There is however a scientific consensus over flux estimates being more accurate, because they include plant physiology analyses and different environmental parameters that control the uptake-that is, not just the exposure-of O3. While O3 is a lot more difficult to measure than other non-reactive greenhouse gases, UV-based and chemiluminescence sensors enable precise and fast measurements and are therefore highly desirable for eddy covariance studies. Using micrometeorological techniques in association with latent heat flux measurements in the field allows for the partition of ozone fluxes into the stomatal and non-stomatal sinks along the soil-plant continuum. Long-term eddy covariance measurements represent a key opportunity in estimating carbon assimilation at high-temporal resolutions, in an effort to study the effect of climate change on photosynthetic mechanisms. Our aim in this work is to describe potential of O3 flux measurement at the canopy level for ozone-risk assessment in established long-term monitoring networks.