Aerobic ammonium-oxidizing bacteria (AerAOB) and anoxic ammonium-oxidizing bacteria (AnAOB) cooperate in partial nitritation/anammox systems to remove ammonium from wastewater. In this process, large granular microbial aggregates enhance the performance, but little is known about granulation so far. In this study, three suspended-growth oxygen-limited autotrophic nitrification-denitrification (OLAND) reactors with different inoculation and operation (mixing and aeration) conditions, designated reactors A, B, and C, were used. The test objectives were (i) to quantify the AerAOB and AnAOB abundance and the activity balance for the different aggregate sizes and (ii) to relate aggregate morphology, size distribution, and architecture putatively to the inoculation and operation of the three reactors. A nitrite accumulation rate ratio (NARR) was defined as the net aerobic nitrite production rate divided by the anoxic nitrite consumption rate. The smallest reactor A, B, and C aggregates were nitrite sources (NARR, >1.7). Large reactor A and C aggregates were granules capable of autonomous nitrogen removal (NARR, 0.6 to 1.1) with internal AnAOB zones surrounded by an AerAOB rim. Around 50% of the autotrophic space in these granules consisted of AerAOB-and AnAOB-specific extracellular polymeric substances. Large reactor B aggregates were thin film-like nitrite sinks (NARR, <0.5) in which AnAOB were not shielded by an AerAOB layer. Voids and channels occupied 13 to 17% of the anoxic zone of AnAOB-rich aggregates (reactors B and C). The hypothesized granulation pathways include granule replication by division and budding and are driven by growth and/or decay based on speciesspecific physiology and by hydrodynamic shear and mixing.In the last few years, autotrophic nitrogen removal via partial nitritation and anoxic ammonium oxidation (anammox) has evolved from lab-to full-scale treatment of nitrogenous wastewaters with a low biodegradable organic compound content, and this evolution has been driven mainly by a significant decrease in the operational costs compared to the costs of conventional nitrification and heterotrophic denitrification (11,23). Oxygen-limited autotrophic nitrification and denitrification (OLAND) is one of the autotrophic processes used and is a one-stage procedure; i.e., partial nitritation and anammox occur in the same reactor (30). The "functional" autotrophic microorganisms in OLAND include aerobic ammonium-oxidizing bacteria (AerAOB) and anoxic ammonium-oxidizing bacteria (AnAOB). With oxygen, AerAOB oxidize ammonium to nitrite (nitritation), and with the nitrite AnAOB oxidize the residual ammonium to form dinitrogen gas and some nitrate (anammox). Additional aerobic nitrite oxidation to nitrate (nitratation) by nitrite-oxidizing bacteria (NOB) lowers the nitrogen removal efficiency, but it can, for instance, be prevented at low dissolved oxygen (DO) levels because the oxygen affinity of AerAOB is higher than that of NOB (16). Reactor configurations for the OLAND process can be based on suspended b...

Summary The main goal of a sustainability assessment is to evaluate the impact of systems (e.g., human or natural ones) on areas sought to be protected and maintained over time (e.g., human well‐being, ecosystems, etc.). These are called areas of protection (AoPs). Life cycle sustainability assessment is a type of sustainability assessment that focuses on the impact of industrial production systems on AoPs. To further this field, three conceptual challenges should be tackled: (1) framing which areas should primarily be sustained and hence on which the impact should be assessed, that is, (re)defining of the AoPs; (2) accounting for the interconnectedness among AoPs (e.g., influence of ecosystems on human well‐being); and (3) the assessment of both benefit and damage to the AoPs (e.g., benefit of industrial products and damage of their production). The aim of this study is to provide a first roadmap to address these three issues and to suggest potential solutions. Regarding the first issue, our conclusion is that human well‐being, encompassing health and happiness, is the primary AoP. This is based on the argument that the sustainability concept is inherently anthropocentric. In this regard, other entities such as ecosystems as a whole are sustained in light of human well‐being. The well‐being adjusted life years, interpreted as years of perfect well‐being, is pinpointed as the most promising holistic indicator. To conduct a respective sustainability assessment that tackles the remaining two issues—integrated system modeling of the earth and its support to well‐being—is argued as the most suitable approach.

To assess the potential environmental impact of human/industrial systems, life cycle assessment (LCA) is a very common method. There are two prominent types of LCA, namely attributional (ALCA) and consequential (CLCA). A lot of literature covers these approaches, but a general consensus on what they represent and an overview of all their differences seems lacking, nor has every prominent feature been fully explored. The two main objectives of this article are: (1) to argue for and select definitions for each concept and (2) specify all conceptual characteristics (including translation into modelling restrictions), re-evaluating and going beyond findings in the state of the art. For the first objective, mainly because the validity of interpretation of a term is also a matter of consensus, we argue the selection of definitions present in the 2011 UNEP-SETAC report. ALCA attributes a share of the potential environmental impact of the world to a product life cycle, while CLCA assesses the environmental consequences of a decision (e.g., increase of product demand). Regarding the second objective, the product system in ALCA constitutes all processes that are linked by physical, energy flows or services. Because of the requirement of additivity for ALCA, a double-counting check needs to be executed, modelling is restricted (e.g., guaranteed through linearity) and partitioning of multifunctional processes is systematically needed (for evaluation per single product). The latter matters also hold in a similar manner for the impact assessment, which is commonly overlooked. CLCA, is completely consequential and there is no limitation regarding what a modelling framework should entail, with the coverage of co-products through substitution being just one approach and not the only one (e.g., additional consumption is possible). Both ALCA and CLCA can be considered over any time span (past, present & future) and either using a reference environment or different scenarios. Furthermore, both ALCA and CLCA could be specific for average or marginal (small) products or decisions, and further datasets. These findings also hold for life cycle sustainability assessment.

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Airborne fine particulate matter (PM) is responsible for the most severe health effects induced by air pollution in Europe. Vegetation, and forests in particular, can play a role in mitigating this pollution since they have a large surface area to filter PM out of the air. Many studies have solely focused on dry deposition of PM onto the tree surface, but deposited PM can be resuspended to the air or may be washed off by precipitation dripping from the plants to the soil. It is only the latter process that represents a net-removal from the atmosphere. To quantify this removal all these processes should be accounted for, which is the case in our modeling framework. Practically, a multilayered PM removal model for forest canopies is developed. In addition, the framework has been integrated into an existing forest growth model in order to account for changes in PM removal efficiency during forest growth. A case study was performed on a Scots pine stand in Belgium (Europe), resulting for 2010 in a dry deposition of 31 kg PM2.5 (PM < 2.5 μm) ha(-1) yr(-1) from which 76% was resuspended and 24% washed off. For different future emission reduction scenarios from 2010 to 2030, with altering PM2.5 air concentration, the avoided health costs due to PM2.5 removal was estimated to range from 915 to 1075 euro ha(-1) yr(-1). The presented model could even be used to predict nutrient input via particulate matter though further research is needed to improve and better validate the model.

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