Advances in sensing ammonia from agricultural sources

Reducing ammonia emissions is one of the most difficult challenges for environmental regulators around the world. About 90% of ammonia in the atmosphere comes from agricultural sources, so that improving farm practices in order to reduce these emissions is a priority. Airborne ammonia is the key precursor for particulate matter (PM2.5) that impairs human health, and ammonia can contribute to excess nitrogen that causes eutrophication in water and biodiversity loss in plant ecosystems. Reductions in excess nitrogen (N) from ammonia are needed so that farms use N resources more efficiently and avoid unnecessary costs. To support the adoption of ammonia emission mitigation practices, new sensor developments are required to identify sources, individual contributions, to evaluate the effectiveness of controls, to monitor progress towards emission-reduction targets, and to develop incentives for behavioural change. There is specifically a need for sensitive, selective, robust and user-friendly sensors to monitor ammonia from livestock production and fertiliser application. Most currently-available sensors need specialists to set up, calibrate and maintain them, which creates issues with staffing and costs when monitoring large areas or when there is a need for high frequency sampling. This paper reports advances in monitoring airborne ammonia in agricultural areas. Selecting the right method of monitoring for each agricultural activity will provide critical data to identify and implement appropriate ammonia controls. Recent developments in chemo-resistive materials allow electrochemical sensing at room temperature, and new spectroscopic methods are sensitive enough to determine low concentrations in the order of parts per billion. However, these new methods still compromise selectivity and sensitivity due to the presence of ambient dust and other interferences, and are not yet suitable to be applied in agricultural monitoring. This review considers how ammonia measurements are made and applied, including the need for sensors that are suitable for routine monitoring by non-specialists. The review evaluates how monitoring information can be used for policies and regulations to mitigate ammonia emissions. The increasing concerns about ammonia emissions and the particular needs from the agriculture sector are addressed, giving an overview of the state-of-the-art and an outlook on future developments

Sources of Airborne Endotoxins in Ambient Air and Exposure of Nearby Communities—A Review

Endotoxin is a bioaerosol component that is known to cause respiratory effects in exposed populations. To date, most research focused on occupational exposure, whilst much less is known about the impact of emissions from industrial operations on downwind endotoxin concentrations. A review of the literature was undertaken, identifying studies that reported endotoxin concentrations in both ambient environments and around sources with high endotoxin emissions. Ambient endotoxin concentrations in both rural and urban areas are generally below 10 endotoxin units (EU) m−3; however, around significant sources such as compost facilities, farms, and wastewater treatment plants, endotoxin concentrations regularly exceeded 100 EU m−3. However, this is affected by a range of factors including sampling approach, equipment, and duration. Reported downwind measurements of endotoxin demonstrate that endotoxin concentrations can remain above upwind concentrations. The evaluation of reported data is complicated due to a wide range of different parameters including sampling approaches, temperature, and site activity, demonstrating the need for a standardised methodology and improved guidance. Thorough characterisation of ambient endotoxin levels and modelling of endotoxin from pollution sources is needed to help inform future policy and support a robust health-based risk assessment process

Air quality and mental health: evidence, challenges and future directions

Background: Poor air quality is associated with poor health. Little attention is given to the complex array of environmental exposures and air pollutants that affect mental health during the life course. // Aims: We gather interdisciplinary expertise and knowledge across the air pollution and mental health fields. We seek to propose future research priorities and how to address them. // Method: Through a rapid narrative review, we summarise the key scientific findings, knowledge gaps and methodological challenges. // Results: There is emerging evidence of associations between poor air quality, both indoors and outdoors, and poor mental health more generally, as well as specific mental disorders. Furthermore, pre-existing long-term conditions appear to deteriorate, requiring more healthcare. Evidence of critical periods for exposure among children and adolescents highlights the need for more longitudinal data as the basis of early preventive actions and policies. Particulate matter, including bioaerosols, are implicated, but form part of a complex exposome influenced by geography, deprivation, socioeconomic conditions and biological and individual vulnerabilities. Critical knowledge gaps need to be addressed to design interventions for mitigation and prevention, reflecting ever-changing sources of air pollution. The evidence base can inform and motivate multi-sector and interdisciplinary efforts of researchers, practitioners, policy makers, industry, community groups and campaigners to take informed action. // Conclusions: There are knowledge gaps and a need for more research, for example, around bioaerosols exposure, indoor and outdoor pollution, urban design and impact on mental health over the life course

Air quality and mental health: evidence, challenges and future directions

Background: Poor air quality is associated with poor health. Little attention is given to the complex array of environmental exposures and air pollutants that affect mental health during the life course. Aims: We gather interdisciplinary expertise and knowledge across the air pollution and mental health fields. We seek to propose future research priorities and how to address them. Method: Through a rapid narrative review, we summarise the key scientific findings, knowledge gaps and methodological challenges. Results: There is emerging evidence of associations between poor air quality, both indoors and outdoors, and poor mental health more generally, as well as specific mental disorders. Furthermore, pre-existing long-term conditions appear to deteriorate, requiring more healthcare. Evidence of critical periods for exposure among children and adolescents highlights the need for more longitudinal data as the basis of early preventive actions and policies. Particulate matter, including bioaerosols, are implicated, but form part of a complex exposome influenced by geography, deprivation, socioeconomic conditions and biological and individual vulnerabilities. Critical knowledge gaps need to be addressed to design interventions for mitigation and prevention, reflecting ever-changing sources of air pollution. The evidence base can inform and motivate multi-sector and interdisciplinary efforts of researchers, practitioners, policy makers, industry, community groups and campaigners to take informed action. Conclusions: There are knowledge gaps and a need for more research, for example, around bioaerosols exposure, indoor and outdoor pollution, urban design and impact on mental health over the life course.Natural Environment Research Council (NERC): NE/V002171/1; Engineering and Physical Sciences Research Council (EPSRC): EP/V052462/1; EP/W001411/1; EP/T003189/

Biomagnetic monitoring of industry-derived particulate pollution.

Clear association exists between ambient PM10 concentrations and adverse health outcomes. However, determination of the strength of associations between exposure and illness is limited by low spatial resolution of particulate concentration measurements. Conventional fixed monitoring stations provide high temporal-resolution data, but cannot capture fine-scale spatial variations. Here we examine the utility of biomagnetic monitoring for spatial mapping of PM10 concentrations around a major industrial site. We combine leaf magnetic measurements with co-located PM10 measurements to achieve intercalibration. Comparison of the leaf-calculated and measured PM10 concentrations with PM10 predictions from a widely-used atmospheric dispersion model indicates that modelling of stack emissions alone substantially under-predicts ambient PM10 concentrations in parts of the study area. Some of this discrepancy might be attributable to fugitive emissions from the industrial site. The composition of the magnetic particulates from vehicle and industry-derived sources differ, indicating the potential of magnetic techniques for source attribution

Rates of particulate pollution deposition onto leaf surfaces: Temporal and inter-species magnetic analyses

Evaluation of health impacts arising from inhalation of pollutant particles <10 mm (PM10) is an active research area. However, lack of exposure data at high spatial resolution impedes identification of causal associations between exposure and illness. Biomagnetic monitoring of PM10 deposited on tree leaves may provide a means of obtaining exposure data at high spatial resolution. To calculate ambient PM10 concentrations from leaf magnetic values, the relationship between the magnetic signal and total PM10 mass must be quantified, and the exposure time (via magnetic deposition velocity (MVd) calculations) known. Birches display higher MVd (w5 cm1) than lime trees (w2 cm1). Leaf saturation remanence values reached ‘equilibrium’ with ambient PM10 concentrations after w6 ‘dry’ days (<3 mm/day rainfall). Other co-located species displayed within-species consistency in MVd; robust inter-calibration can thus be achieved, enabling magnetic PM10 biomonitoring at unprecedented spatial resolution

Rapid magnetic biomonitoring and differentiation of atmospheric particulate pollutants at the roadside and around two major industrial sites, U.K..

Emissions of particulate matter (PM) from vehicle and industrial sources constitute a hazard to human health. Here, we apply biomagnetic monitoring to a) discriminate between potential PM10 sources around a steelworks, and b) examine magnetic source differentiation for a combined, U.K.-based, magnetic dataset (steelworks, roadside, power-generating site). Tree leaves (sampled September 2009, as passive PM receptors) and putative sources were subjected to rapid magnetic characterisation (magnetic remanence measurements). Fuzzy cluster analysis of the combined dataset identified three clusters, showing that particulates emitted from vehicle fleets (e.g. diesel/petrol), and from different industrial processes can be magnetically differentiated. Cluster analysis of the steelworks leaf receptors and potential sources identified seven magnetic groupings. Leaves from one PM ‘hotspot’ showed no affinity with any available source sample, suggesting an as yet untested PM source. These data indicate the value of fast, inexpensive magnetic techniques for particulate source discrimination and indication of ‘missing’ sources

Airborne microplastic monitoring: Developing a simplified outdoor sampling approach using pollen monitoring equipment

A novel, yet simple, airborne microplastic (MP) sampling approach using global pollen monitoring equipment was applied to identify, characterise and quantify outdoor airborne MPs for the first time. Modification of Burkard spore trap tape adhesive provided particle capture and facilitated downstream spectroscopy analysis. 36 polymer types were identified from a total of 21 days sampling using Burkard spore traps at two locations (United Kingdom and South Africa). MPs were detected in 95 % of daily samples. Mean MP particle levels were 2.0 ± 0.9 MP m-3 (11 polymer types) in Hull (U.K.), during March, 2.9 ± 2.0 MP m-3 (16 types) in Hull in July, and 11.0 ± 5.7 MP m-3 (29 types) in Gqeberha, (S.A.) in August 2023. The most abundant polymer type was nylon (Gqeberha). The approach was compared with two passive sampling methods whereby 27 polymer types were identified and of these, 6 types were above the limit of quantification (LOQ), with poly(methacrolein:styrene) (PMA/PS) the most abundant. Irregularly shaped MPs < 100 µm in length were predominant from all sampling approaches. For the first time, airborne MPs were chemically characterised and quantified using volumetric pollen sampling equipment, representing a viable approach for future airborne MP monitoring