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The fuel sulfur content (FSC) of ocean-going and inland vessels was measured simultaneously by eight different state-of-the-art and novel monitoring systems during a 6-week campaign at the Elbe River, at a distance of about 10 km to the port of Hamburg, Germany. Both stationary and airborne systems on unoccupied aerial vehicles (UAVs) were operated by four participating partners in a side-by-side measurement setup to measure the emission factors of the same emission sources. A novel laser spectrometer, with significantly better-precision specifications as compared with the other instruments, was used for the first time for emission monitoring regarding the International Convention for the Prevention of Pollution from Ships (MARPOL) Annex VI regulations. The comparison took place in the North Sea sulfur emission control area (SECA), where the allowed FSC is limited to 0.10 %Sm/m. The unit %Sm/m relates to the percentage of mass sulfur per mass combusted fuel. In total, 966 plumes that originated from 436 different vessels were analysed in this study. At the same time, fuel samples obtained from 34 different vessels and bunker delivery notes (BDNs) from five frequently monitored vessels were used as a reference to assess the uncertainties of the different systems. Seven of the eight measurement systems tended to underestimate the FSC found from fuel samples and BDNs. A possible relation between underestimation and high relative humidities (above 80 %) was observed. The lowest systematic deviations were observed for the airborne systems and the novel laser spectrometer. The two UAV-borne systems showed total uncertainties of 0.07 %Sm/m and 0.09 %Sm/m (confidence level: 95 %). The novel laser spectrometer showed the lowest total uncertainty of 0.05 %Sm/m compared with other stationary sniffer systems, whose total uncertainties range from 0.08 %Sm/m to 0.09 %Sm/m. It was concluded that non-compliant vessels, with an actual FSC of the combusted fuel above 0.15 %Sm/m to 0.19 %Sm/m, can be detected by the compared systems with 95 % confidence.

Methods for the determination of ship fuel sulphur content and NOx emission factors based on remote measurements have been compared in the harbour of Rotterdam and compared to direct stack emission measurements on the ferry Stena Hollandica. The methods were selected based on a review of the available literature on ship emission measurements. They were either optical (LIDAR, Differential Optical Absorption Spectroscopy (DOAS), UV camera), combined with model-based estimates of fuel consumption, or based on the so called "sniffer" principle, where SO2 or NOx emission factors are determined from simultaneous measurement of the increase of CO2 and SO2 or NOx concentrations in the plume of the ship compared to the background. The measurements were performed from stations at land, from a boat and from a helicopter. Mobile measurement platforms were found to have important advantages compared to the land-based ones because they allow optimizing the sampling conditions and sampling from ships on the open sea. Although optical methods can provide reliable results it was found that at the state of the art level, the "sniffer" approach is the most convenient technique for determining both SO2 and NOx emission factors remotely. The average random error on the determination of SO2 emission factors comparing two identical instrumental set-ups was 6%. However, it was found that apparently minor differences in the instrumental characteristics, such as response time, could cause significant differences between the emission factors determined. Direct stack measurements showed that about 14% of the fuel sulphur content was not emitted as SO2. This was supported by the remote measurements and is in agreement with the results of other field studies.

The chemical composition of the plumes of seagoing ships was measured during a two week long measurement campaign in the port of Rotterdam, Hoek van Holland The Netherlands, in September 2009. Altogether, 497 ships were monitored and a statistical evaluation of emission factors (g kg−1 fuel) was provided. The concerned main atmospheric components were SO2, NO2, NOx and the aerosol particle number. In addition, the elemental and water-soluble ionic composition of the emitted particulate matter was determined. Emission factors were expressed as a function of ship type, power and crankshaft rotational speed. The average SO2 emission factor was found to be roughly half of what is allowed in sulphur emission control areas (16 vs. 30 g kg−1 fuel), and exceedances of this limit were rarely registered. A significant linear relationship was observed between the SO2 and particle number emission factors. The intercept of the regression line, 4.8 × 1015 (kg fuel)−1, gives the average number of particles formed during the burning of 1 kg zero sulphur content fuel, while the slope, 2 × 1018, provides the average number of particles formed with 1 kg sulphur burnt with the fuel. Water-soluble ionic composition analysis of the aerosol samples from the plumes showed that ~144 g of particulate sulphate was emitted from 1 kg sulphur burnt with the fuel. The mass median diameter of sulphate particles estimated from the measurements was ~42 nm.

Soil emissions of NO and N2O were measured continuously at high frequency for more than one year at 15 European forest sites as part of the EU-funded project NOFRETETE. The locations represent different forest types (coniferous/deciduous) and different nitrogen loads. Geographically they range from Finland in the north to Italy in the south and from Hungary in the east to Scotland in the west. The highest NO emissions were observed from coniferous forests, whereas the lowest NO emissions were observed from deciduous forests. The NO emissions from coniferous forests were highly correlated with N-deposition. The site with the highest average annual emission (82 μg NO-N m−2 h−1) was a spruce forest in South-Germany (Höglwald) receiving an annual N-deposition of 2.9 g m−2. NO emissions close to the detection limit were observed from a pine forest in Finland where the N-deposition was 0.2 g N m−2 a−1. No significant correlation between N2O emission and N-deposition was found. The highest average annual N2O emission (20 μg N2O-N m−2 h−1) was found in an oak forest in the Mátra mountains (Hungary) receiving an annual N-deposition of 1.6 g m−2. N2O emission was significantly negatively correlated with the C/N ratio. The difference in N-oxide emissions from soils of coniferous and deciduous forests may partly be explained by differences in N-deposition rates and partly by differences in characteristics of the litter layer and soil. NO was mainly derived from nitrification whereas N2O was mainly derived from denitrification. In general, soil moisture is lower at coniferous sites (at least during spring time) and the litter layer of coniferous forests is thick and well aerated favouring nitrification and thus release of NO. Conversely, the higher rates of denitrification in deciduous forests due to a compact and moist litter layer lead to N2O production and NO consumption in the soil. The two factors soil moisture and soil temperature are often explaining most of the temporal variation within a site. When comparing annual emissions on a regional scale, however, factors such as nitrogen deposition and forest and soil type become much more important.

Forest soils are a significant source for the primary and secondary greenhouse gases N2O and NO. However, current estimates are still uncertain due to the still limited number of field measurements and the herein observed pronounced variability of N trace gas fluxes in space and time, which are due to the variation of environmental factors such as soil and vegetation properties or meteorological conditions. To overcome these problems we further developed a process-oriented model, the PnET-N-DNDC model, which simulates the N trace gas exchange on the basis of the processes involved in production, consumption and emission of N trace gases. This model was validated against field observations of N trace gas fluxes from 19 sites obtained within the EU project NOFRETETE, and shown to perform well for N2O (r2=0.68, slope=0.76) and NO (r2=0.78, slope=0.73). For the calculation of a European-wide emission inventory we linked the model to a detailed, regionally and temporally resolved database, comprising climatic properties (daily resolution), and soil parameters, and information on forest areas and types for the years 1990, 1995 and 2000. Our calculations show that N trace gas fluxes from forest soils may vary substantial from year to year and that distinct regional patterns can be observed. Our central estimate of NO emissions from forest soils in the EU amounts to 98.4, 84.9 and 99.2 kt N yr−1, using meteorology from 1990, 1995 and year 2000, respectively. This is x emissions. For N2O emissions the central estimates were 86.8, 77.6 and 81.6 kt N yr−1, respectively, which is approx. 14.5% of the source strength coming from agricultural soils. An extensive sensitivity analysis was conducted which showed a range in emissions from 44.4 to 254.0 kt N yr−1 for NO and 50.7 to 96.9 kt N yr−1 for N2O, for year 2000 meteorology. The results show that process-oriented models coupled to a GIS are useful tools for the calculation of regional, national, or global inventories of biogenic N trace gas emissions from soils. This work represents the most comprehensive effort to date to simulate NO and N2O emissions from European forest soils.

Simulation of emissions of the greenhouse gas N2O from agricultural land is still a challenge. This is mainly due to its high temporal variability, with low background emissions and a few transient peaks. In this study, a first attempt was made to simulate observations of N2O fluxes with a daily time step from managed peat soils. We used a new N2O module added to the extensively tested hydrological-biogeochemical model combination SWAP-ANIMO, hypothesizing that accurate simulation of the controlling factors would imply accurate simulation of the dynamics of the N2O emissions as well. We used daily N2O emission data from three sites in the Netherlands, with complementary data on soil moisture, mineral N content, and soil N2O concentration. Simulation of soil moisture, mineral N, and N2O concentration was reasonable to good. Still, simulation of the daily N2O emissions was poor, with model efficiencies <0, mainly due to overestimation of N2O emission from denitrification in the topsoil. We expect that the model overestimated diffusion of N2O from the topsoil to the atmosphere, thereby underestimating further reductions of N2O to N2 This was probably linked to the complex peat pore geometry. We concluded that the description of the N2O processes in our model was insufficient to accurately simulate daily N2O emissions from peat soils, even when the main controlling factors are accurately simulated. Improvement is expected from incorporation of the effects of (peat) pore geometry on soil moisture and consequently on N2O production, reduction, storage, and transport. [ABSTRACT FROM AUTHOR]

Background: Pesticides can be transported from the site of application to homes via different routes and lead to exposure of residents, raising concerns regarding health effects. We built a deterministic model framework (OBOmod) to assess exposure of residents living near fields where pesticides are applied., Methods: OBOmod connects five independent models operating on an hourly timescale and high spatial resolution (meters). Models include descriptions of spray drift, volatilization, atmospheric transport and dispersion, exchange between outdoor and indoor air and exchange between indoor air and dust. Fourteen bulb field applications under different weather conditions and comprising 12 pesticides were simulated. Each simulation included the first seven days after the application. The concentrations computed with OBOmod were compared with those measured in outdoor and indoor air and the amounts measured in indoor dust samples., Results: Model evaluation indicated suitability of the developed framework to estimate outdoor and indoor air concentrations. For most pesticides, model accuracy was good. The framework explained about 30% to 95% of the temporal and spatial variability of air concentrations. For 20% of the simulations, the framework explained more than 35% of spatial variability of concentrations in dust. In general, OBOmod estimates remained within one order of magnitude from measured levels. Calculations showed that in addition to spray drift during application, volatilization from the field after spraying and pesticides in house dust are important routes for residents' exposure to pesticides., Conclusions: Our framework covers many processes needed to calculate exposure of residents to pesticides. The evaluation phase shows that, with the exception of the dust model, the framework can be used in support of health and epidemiological studies, and can serve as a tool to support development of regulations and policy making regarding pesticide use., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Indoor dust has been postulated as an important matrix for residential pesticide exposure. However, there is a lack of information on presence, concentrations and determinants of multiple pesticides in dust in residential homes close to treated fields. Our objective was to characterize the spatial and temporal variance of pesticides in house dust, study the use of doormats and floors as proxies for pesticides in indoor dust and identify determinants of occurrence and concentrations. Homes within 250 m from selected bulb fields were invited to participate. Homes within 20 km from these fields but not having agricultural fields within 500 m were selected as controls. House dust was vacuumed in all homes from floors (VFD) and from newly placed clean doormats (DDM). Sampling was done during two periods, when pesticides are used and not-used. For determination of 46 prioritized pesticides, a multi-residue extraction method was used. Most statistical analyses are focused on the 12 and 14 pesticides that were detected in >40% of DDM and VFD samples, respectively. Mixed models were used to evaluate relationships between possible determinants and pesticides occurrence and concentrations in DDM and VFD. 17 pesticides were detected in more than 50% of the homes in both matrixes. Concentrations differed by about a factor five between use and non-use periods among homes within 250 m of fields and between these homes and controls. For 7 pesticides there was a moderate to strong correlation (Spearman rho 0.30-0.75) between concentrations in DDM and VFD. Distance to agricultural fields and air concentrations were among the most relevant predictors for occurrence and levels of a given pesticide in DDM. Concentrations in dust are overall higher during application periods and closer to fields (<250 m) than further away. The omnipresence of pesticides in dust lead to residents being exposed all year round., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Residential exposure to pesticides may occur via inhalation of airborne pesticides, direct skin contacts with pesticide-contaminated surfaces, and consumption of food containing pesticide residues. The aim was to study the association of dermal exposure to pesticides between the use and non-use periods, between farmer and non-farmer families and between dermal exposure and the excretion of metabolites from urine in residents living close to treated agricultural fields., Methods: In total, 112 hand wipes and 206 spot urine samples were collected from 16 farmer and 38 non-farmer participants living within 50 m from an agricultural field in the Netherlands. The study took place from May 2016 to December 2017 during the use as well as the non-use periods of pesticides. Hand wipes were analysed for the parent compound and urines samples for the corresponding urinary metabolite of five applied pesticides: asulam, carbendazim (applied as thiophanate-methyl), chlorpropham, prochloraz and tebuconazole. Questionnaire data was used to study potential determinants of occurrence and levels of pesticides in hand wipes according to univariate and multivariate analysis., Results: Carbendazim and tebuconazole concentrations in hand wipes were statistically significantly higher in the pesticide-use period compared to the non-use period. In addition, especially during the use periods, concentrations were statistically significantly higher in farmer families compared to non-farmer families. For asulam, chlorpropham and prochloraz, the frequency of non-detects was too high (57-85%) to be included in this analysis. The carbendazim contents in urine samples and hand wipes were correlated on the first and second day after taking the hand wipe, whereas chlorpropham was only observed to be related on the second day following the spray event., Conclusions: Concentrations in hand wipes were overall higher in pesticide use periods compared to non-use periods and higher in farmer families compared to non-farmer families. Only for carbendazim a strong correlation between concentrations in hand wipes and its main metabolite in urine was observed, indicating dermal exposure via contaminated indoor surfaces. We expect this to be related to the lower vapour pressure and longer environmental lifetime of carbendazim compared to the other pesticides studies., (Copyright © 2021. Published by Elsevier Inc.)

Background: Application of pesticides in the vicinity of homes has caused concern regarding possible health effects in residents living nearby. However, the high spatiotemporal variation of pesticide levels and lack of knowledge regarding the contribution of exposure routes greatly complicates exposure assessment approaches., Objective: The objective of this paper was to describe the study protocol of a large exposure survey in the Netherlands assessing pesticide exposure of residents living close (<250 m) to agricultural fields; to better understand possible routes of exposure; to develop an integrative exposure model for residential exposure; and to describe lessons learned., Methods: We performed an observational study involving residents living in the vicinity of agricultural fields and residents living more than 500 m away from any agricultural fields (control subjects). Residential exposures were measured both during a pesticide use period after a specific application and during the nonuse period for 7 and 2 days, respectively. We collected environmental samples (outdoor and indoor air, dust, and garden and field soils) and personal samples (urine and hand wipes). We also collected data on spraying applications as well as on home characteristics, participants' demographics, and food habits via questionnaires and diaries. Environmental samples were analyzed for 46 prioritized pesticides. Urine samples were analyzed for biomarkers of a subset of 5 pesticides. Alongside the field study, and by taking spray events and environmental data into account, we developed a modeling framework to estimate environmental exposure of residents to pesticides., Results: Our study was conducted between 2016 and 2019. We assessed 96 homes and 192 participants, including 7 growers and 28 control subjects. We followed 14 pesticide applications, applying 20 active ingredients. We collected 4416 samples: 1018 air, 445 dust (224 vacuumed floor, 221 doormat), 265 soil (238 garden, 27 fields), 2485 urine, 112 hand wipes, and 91 tank mixtures., Conclusions: To our knowledge, this is the first study on residents' exposure to pesticides addressing all major nondietary exposure sources and routes (air, soil, dust). Our protocol provides insights on used sampling techniques, the wealth of data collected, developed methods, modeling framework, and lessons learned. Resources and data are open for future collaborations on this important topic., International Registered Report Identifier (irrid): RR1-10.2196/27883., (©Daniel M Figueiredo, Esmeralda J M Krop, Jan Duyzer, Rianda M Gerritsen-Ebben, Yvonne M Gooijer, Henk J Holterman, Anke Huss, Cor M J Jacobs, Carla M Kivits, Roel Kruijne, Hans J G J Mol, Arné Oerlemans, Pieter J J Sauer, Paul T J Scheepers, Jan C van de Zande, Erik van den Berg, Marcel Wenneker, Roel C H Vermeulen. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 28.04.2021.)

This commentary explores the use of high-resolution data from new, miniature sensors to enrich models that predict exposures to chemical substances in the workplace. To optimally apply these sensors, one can expect an increased need for new models that will facilitate the interpretation and extrapolation of the acquired time-resolved data. We identified three key modelling approaches in the context of sensor data, namely (i) enrichment of existing time-integrated exposure models, (ii) (new) high-resolution (in time and space) empirical models, and (iii) new 'occupational dispersion' models. Each approach was evaluated in terms of their application in research, practice, and for policy purposes. It is expected that substance-specific sensor data will have the potential to transform workplace modelling by re-calibrating, refining, and validating existing (time-integrated) models. An increased shift towards 'sensor-driven' models is expected. It will allow for high-resolution modelling in time and space to identify peak exposures and will be beneficial for more individualized exposure assessment and real-time risk management. New 'occupational dispersion models' such as interpolation, computational fluid dynamic models, and assimilation techniques, together with sensor data, will be specifically useful. These techniques can be applied to develop site-specific concentration maps which calculate personal exposures and mitigate worker exposure through early warning systems, source finding and improved control design and control strategies. Critical development and investment needs for sensor data linked to (new) model development were identified such as (i) the generation of more sensor data with reliable sensor technologies (achieved by improved specificity, sensitivity, and accuracy of sensors), (ii) investing in statistical and new model developments, (iii) ensuring that we comply with privacy and security issues of concern, and (iv) acceptance by relevant target groups (such as employers and employees) and stimulation of these new technologies by policymakers and technology developers., (© The Author(s) 2020. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.)

We developed a spatio-temporal model for the Netherlands to estimate environmental exposure to individual agricultural pesticides at the residential address for application in a national case-control study on Parkinson's disease (PD). Data on agricultural land use and pesticide use were combined to estimate environmental exposure to pesticides for the period 1961 onwards. Distance categories of 0-50 m, >50-100 m, >100-500 m and >500-1000 m around residences were considered. For illustration purposes, exposure was estimated for the control population (n=607) in the PD case-control study. In a small validation effort, model estimates were compared with pesticide measurements in air and precipitation collected at 17 stations in 2000-2001. Estimated exposure prevalence was higher for pesticides used on commonly cultivated (rotating) crops than for pesticides used on fruit and bulbs only. Prevalence increased with increasing distance considered. Moderate-to-high correlations were observed between model estimates (>100-500 m and >500-1000 m) and environmental pesticide concentrations measured in 2000-2001. Environmental exposure to individual pesticides can be estimated using relevant spatial and temporal data sets on agricultural land use and pesticide use. Our approach seems to result in accurate estimates of average environmental exposure, although it remains to be investigated to what extent this reflect personal exposure to agricultural pesticides.

Atmospheric deposition may be an important source of persistent organic compounds (POP) and pesticides for the Dutch coastal and inland waters. Current estimates of the atmospheric input have been made using atmospheric dispersion models. The uncertainty is however large. A project was defined with the aim to assess the input on the basis of measurements. For a period of two years (1999-2001) a monitoring network was operated. At eighteen stations, located across the whole country, air and precipitation samples were taken on a weekly and monthly basis. In these samples the concentrations of pesticides, PCB's and PAH's were determined. Up to 50 different pesticides were observed in precipitation and air. The concentration of 17 of these in precipitation exceeded the maximum permissible level for surface water and 22 exceeded the standard for drinking water of 100 ng l(-1). The input from the atmosphere to Dutch inland waters appeared to be as large as the input of pesticides by other sources such as spray drift. Model calculations were also carried out to identify the sources of these compounds. The occurrence of atrazine could be related to emissions outside the Netherlands.

In the context of international efforts to reduce the impacts of atmospheric NH3 and NH4+ (collectively, NHx). it is important to establish the link between NH3 emissions and monitoring of NHx concentrations and deposition. This is equally relevant to situations where NH3 emissions changes are certain (e.g. due to changed source sector activity), as to cases where NH3 abatement technologies have been implemented. Correct interpretation of adequate atmospheric measurements is essential, since monitoring data provide the only means to evaluate trends in regional NH3 emissions. These issues have been reviewed using available measurements and modelling from nine countries. In addition to historic datasets, the analysis here considers countries where NH3 source sector activity changed (both increases and decreases) and countries where NH3 abatement policies have been implemented. In The Netherlands an 'ammonia gap' was identified between the expected reduction and results of monitoring, and was attributed initially to ineffectiveness of the abatement measures. The analysis here for a range of countries shows that atmospheric interactions complicate the expected changes, particularly since SO2 emissions have decreased at the same time, while at many sites the few years of available data show substantial inter-annual variation. It is concluded that networks need to be established that speciate between NH3 and aerosol NH4+, in addition to providing wet deposition, and sample at sufficient sites for robust regional estimates to be established. Such measurements will be essential to monitor compliance of the international agreements on NH3 emission abatement.