Source Attribution of Health Burdens From Ambient PM2.5, O3, and NO2 Exposure for Assessment of South Korean National Emission Control Scenarios by 2050.

We quantify anthropogenic sources of health burdens associated with ambient air pollution exposure in South Korea and forecast future health burdens using domestic emission control scenarios by 2050 provided by the United Nations Environment Programme (UNEP). Our health burden estimation framework uses GEOS-Chem simulations, satellite-derived NO2, and ground-based observations of PM2.5, O3, and NO2. We estimate 19,000, 3,300, and 8,500 premature deaths owing to long-term exposure to PM2.5, O3, and NO2, respectively, and 23,000 NO2-associated childhood asthma incidences in 2016. Next, we calculate anthropogenic emission contributions to these four health burdens from each species and grid cell using adjoint sensitivity analysis. Domestic sources account for 56%, 38%, 87%, and 88% of marginal emission contributions to the PM2.5-, O3-, and NO2-associated premature deaths and the NO2-associated childhood asthma incidences, respectively. We project health burdens to 2050 using UNEP domestic emission scenarios (Baseline and Mitigation) and population forecasts from Statistics Korea. Because of population aging alone, there are 41,000, 10,000, and 20,000 more premature deaths associated with PM2.5, O3, and NO2 exposure, respectively, and 9,000 fewer childhood asthma incidences associated with NO2. The Mitigation scenario doubles the NO2-associated health benefits over the Baseline scenario, preventing 24,000 premature deaths and 13,000 childhood asthma incidences by 2050. It also slightly reduces PM2.5- and O3-associated premature deaths by 9.9% and 7.0%, unlike the Baseline scenario where these pollutants increase. Furthermore, we examine foreign emission impacts from nine SSP/RCP-based scenarios, highlighting the need for international cooperation to reduce PM2.5 and O3 pollution.

Improving consistency in estimating future health burdens from environmental risk factors: Case study for ambient air pollution.

Future changes in exposure to risk factors should impact mortality rates and population. However, studies commonly use mortality rates and population projections developed exogenously to the health impact assessment model used to quantify future health burdens attributable to environmental risks that are therefore invariant to projected exposure levels. This impacts the robustness of many future health burden estimates for environmental risk factors. This work describes an alternative methodology that more consistently represents the interaction between risk factor exposure, population and mortality rates, using ambient particulate air pollution (PM2.5) as a case study. A demographic model is described that estimates future population based on projected births, mortality and migration. Mortality rates are disaggregated between the fraction due to PM2.5 exposure and other factors for a historic year, and projected independently. Accounting for feedbacks between future risk factor exposure and population and mortality rates can greatly affect estimated future attributable health burdens. The demographic model estimates much larger PM2.5-attributable health burdens with constant 2019 PM2.5 (∼10.8 million deaths in 2050) compared to a model using exogenous population and mortality rate projections (∼7.3 million), largely due to differences in mortality rate projection methods. Demographic model-projected PM2.5-attributable mortality can accumulate substantially over time. For example, ∼71 million more people are estimated to be alive in 2050 when WHO guidelines (5 µg m-3) are achieved compared to constant 2019 PM2.5 concentrations. Accounting for feedbacks is more important in applications with relatively high future PM2.5 concentrations, and relatively large changes in non-PM2.5 mortality rates.

Air pollution mitigation assessment to inform Cambodia's first clean air plan.

Cambodia's 16.5 million people are exposed to air pollution in excess of World Health Organisation guidelines. The Royal Government of Cambodia has regulated air pollutant emissions and concentrations since 2000, but rapid economic growth and energy consumption means air pollution continues to impact human health. In December 2021, the Ministry of Environment of Cambodia published Cambodia's first Clean Air Plan that outlines actions to reduce air pollutant emissions over the next decade. This work presents the quantitative air pollution mitigation assessment underpinning the identification and evaluation of measures included in Cambodia's Clean Air Plan. Historic emissions of particulate matter (PM2.5, black carbon, organic carbon) and gaseous (nitrogen oxides, volatile organic compounds, sulphur dioxide, ammonia, and carbon monoxide) air pollutants are quantified between 2010 and 2015, and projected to 2030 for a baseline scenario. Mitigation scenarios reflecting implementation of 14 measures included in Cambodia's Clean Air Plan were modelled, to quantify the national reduction in emissions, from which the reduction in ambient PM2.5 exposure and attributable health burdens were estimated. In 2015, the residential, transport, and waste sectors contribute the largest fraction of national total air pollutant emissions. Without emission reduction measures, air pollutant emissions could increase by between 50 and 150% in 2030 compared to 2015 levels, predominantly due to increases in transport emissions. The implementation of the 14 mitigation measures could substantially reduce emissions of all air pollutants, by between 60 and 80% in 2030 compared to the baseline. This reduction in emissions was estimated to avoid approximately 900 (95% C.I.: 530-1200) premature deaths per year in 2030 compared to the baseline scenario. In addition to improving air pollution and public health, Cambodia's Clean Air Plan could also to lead to additional benefits, including a 19% reduction in carbon dioxide emissions, simultaneously contributing to Cambodia's climate change goals.

Integrated climate change and air pollution mitigation assessment for Togo.

Togo, in west Africa, is vulnerable to the impacts of climate change, but has made a negligible contribution to causing it. Togo ratified the Paris Agreement in 2017, committing to submit Nationally Determined Contributions (NDCs) that outline Togo's climate change mitigation commitment. Togo's capital, Lomé, as well as other areas of Togo have ambient air pollutant levels exceeding World Health Organisation guidelines for human health protection, and 91 % of Togolese households cook using solid biomass, elevating household air pollution exposure. In Togo's updated NDC, submitted in 2021, Togo acknowledges the importance and opportunity of achieving international climate change mitigation targets in ways that improve air quality and achieve health benefits for Togo's citizens. The aim of this work is to evaluate priority mitigation measures in an integrated assessment of air pollutant, Short-Lived Climate Pollutant (SLCP) and Greenhouse Gas (GHG) emissions to identify their effectiveness in simultaneously reducing air pollution and Togo's contribution to climate change. The mitigation assessment quantifies emissions for Togo and Grand Lomé from all major source sectors for historical years between 2010 and 2018, for a baseline projection to 2030 and for mitigation scenarios evaluating ten mitigation measures. The assessment estimates that Togo emitted ~21 million tonnes of GHG emissions in 2018, predominantly from the energy and Agriculture, Forestry and Other Land Use sectors. GHG emissions are projected to increase 42 % to 30 million tonnes in 2030 without implementation of mitigation policies and measures. The implementation of the ten identified priority mitigation measures could reduce GHG emissions by ~20 % in 2030 compared to the baseline, while SLCPs and air pollutants were estimated to be reduced more, with a more than 75 % reduction in black carbon emissions in 2030. This work therefore provides a clear pathway by which Togo can reduce its already small contribution to climate change while simultaneously achieving local benefits for air quality and human health in Togo and Grand Lomé.

A rapid review of the impact of COVID-19 on the mental health of healthcare workers: implications for supporting psychological well-being.

BACKGROUND: Health and social care workers (HSCWs) have carried a heavy burden during the COVID-19 crisis and, in the challenge to control the virus, have directly faced its consequences. Supporting their psychological wellbeing continues, therefore, to be a priority. This rapid review was carried out to establish whether there are any identifiable risk factors for adverse mental health outcomes amongst HSCWs during the COVID-19 crisis. METHODS: We undertook a rapid review of the literature following guidelines by the WHO and the Cochrane Collaboration's recommendations. We searched across 14 databases, executing the search at two different time points. We included published, observational and experimental studies that reported the psychological effects on HSCWs during the COVID-19 pandemic. RESULTS: The 24 studies included in this review reported data predominantly from China (18 out of 24 included studies) and most sampled urban hospital staff. Our study indicates that COVID-19 has a considerable impact on the psychological wellbeing of front-line hospital staff. Results suggest that nurses may be at higher risk of adverse mental health outcomes during this pandemic, but no studies compare this group with the primary care workforce. Furthermore, no studies investigated the psychological impact of the COVID-19 pandemic on social care staff. Other risk factors identified were underlying organic illness, gender (female), concern about family, fear of infection, lack of personal protective equipment (PPE) and close contact with COVID-19. Systemic support, adequate knowledge and resilience were identified as factors protecting against adverse mental health outcomes. CONCLUSIONS: The evidence to date suggests that female nurses with close contact with COVID-19 patients may have the most to gain from efforts aimed at supporting psychological well-being. However, inconsistencies in findings and a lack of data collected outside of hospital settings, suggest that we should not exclude any groups when addressing psychological well-being in health and social care workers. Whilst psychological interventions aimed at enhancing resilience in the individual may be of benefit, it is evident that to build a resilient workforce, occupational and environmental factors must be addressed. Further research including social care workers and analysis of wider societal structural factors is recommended.

Development of the Low Emissions Analysis Platform - Integrated Benefits Calculator (LEAP-IBC) tool to assess air quality and climate co-benefits: Application for Bangladesh.

Low- and middle-income countries have the largest health burdens associated with air pollution exposure, and are particularly vulnerable to climate change impacts. Substantial opportunities have been identified to simultaneously improve air quality and mitigate climate change due to overlapping sources of greenhouse gas and air pollutant emissions and because a subset of pollutants, short-lived climate pollutants (SLCPs), directly contribute to both impacts. However, planners in low- and middle-income countries often lack practical tools to quantify the air pollution and climate change impacts of different policies and measures. This paper presents a modelling framework implemented in the Low Emissions Analysis Platform - Integrated Benefits Calculator (LEAP-IBC) tool to develop integrated strategies to improve air quality, human health and mitigate climate change. The framework estimates emissions of greenhouse gases, SLCPs and air pollutants for historical years, and future projections for baseline and mitigation scenarios. These emissions are then used to quantify i) population-weighted annual average ambient PM2.5 concentrations across the target country, ii) household PM2.5 exposure of different population groups living in households cooking using different fuels/technologies and iii) radiative forcing from all emissions. Health impacts (premature mortality) attributable to ambient and household PM2.5 exposure and changes in global average temperature change are then estimated. This framework is applied in Bangladesh to evaluate the air quality and climate change benefits from implementation of Bangladesh's Nationally Determined Contribution (NDC) and National Action Plan to reduce SLCPs. Results show that the measures included to reduce GHGs in Bangladesh's NDC also have substantial benefits for air quality and human health. Full implementation of Bangladesh's NDC, and National SLCP Plan would reduce carbon dioxide, methane, black carbon and primary PM2.5 emissions by 25%, 34%, 46% and 45%, respectively in 2030 compared to a baseline scenario. These emission reductions could reduce population-weighted ambient PM2.5 concentrations in Bangladesh by 18% in 2030, and avoid approximately 12,000 and 100,000 premature deaths attributable to ambient and household PM2.5 exposures, respectively, in 2030. As countries are simultaneously planning to achieve the climate goals in the Paris Agreement, improve air quality to reduce health impacts and achieve the Sustainable Development Goals, the LEAP-IBC tool provides a practical framework by which planners can develop integrated strategies, achieving multiple air quality and climate benefits.

Tropospheric ozone assessment report: Global ozone metrics for climate change, human health, and crop/ecosystem research.

Assessment of spatial and temporal variation in the impacts of ozone on human health, vegetation, and climate requires appropriate metrics. A key component of the Tropospheric Ozone Assessment Report (TOAR) is the consistent calculation of these metrics at thousands of monitoring sites globally. Investigating temporal trends in these metrics required that the same statistical methods be applied across these ozone monitoring sites. The nonparametric Mann-Kendall test (for significant trends) and the Theil-Sen estimator (for estimating the magnitude of trend) were selected to provide robust methods across all sites. This paper provides the scientific underpinnings necessary to better understand the implications of and rationale for selecting a specific TOAR metric for assessing spatial and temporal variation in ozone for a particular impact. The rationale and underlying research evidence that influence the derivation of specific metrics are given. The form of 25 metrics (4 for model-measurement comparison, 5 for characterization of ozone in the free troposphere, 11 for human health impacts, and 5 for vegetation impacts) are described. Finally, this study categorizes health and vegetation exposure metrics based on the extent to which they are determined only by the highest hourly ozone levels, or by a wider range of values. The magnitude of the metrics is influenced by both the distribution of hourly average ozone concentrations at a site location, and the extent to which a particular metric is determined by relatively low, moderate, and high hourly ozone levels. Hence, for the same ozone time series, changes in the distribution of ozone concentrations can result in different changes in the magnitude and direction of trends for different metrics. Thus, dissimilar conclusions about the effect of changes in the drivers of ozone variability (e.g., precursor emissions) on health and vegetation exposure can result from the selection of different metrics.

Engaging military couples in marital research: does requesting referrals from service members to recruit their spouses introduce sample bias?

BACKGROUND: While enrolling dyads in research studies is not uncommon, there is limited literature on the utility of different recruitment strategies and the resulting selection biases. This paper examined two recruitment strategies used to enroll military couples in a longitudinal study, assessing the impact of both strategies on the representativeness of the final study sample. METHOD: Descriptive and bivariate analyses were conducted to 1) identify characteristics associated with spouse referral, 2) compare response rates based on recruitment strategy and assess whether recruitment strategy modified correlates of response propensity among spouses, and 3) assess whether referred spouse characteristics differed from non-referred spouses in the final sample. The study sample consisted of married US service members with 2-5 years of military service as of October 2011 and their spouses. RESULTS: Service members who referred their spouses to participate in the Millennium Cohort Family Study were more likely to be male, have children, serve in the Army, and have combat deployment experience than those who did not refer their spouse. Nearly two-thirds (n = 5331, 64.9%) of referred spouses participated in the Family Study, compared with less than one-third (n = 3458, 29.5%) of directly contacted spouses. Spouse characteristics also differed significantly between recruitment groups. CONCLUSIONS: Overall results suggest that minimal bias was introduced by using a referral recruitment methodology. Service members appeared to be more likely to refer their spouses if they perceived the research topic as relevant to their spouse, such that male service members with combat deployment experience were more likely to refer female spouses caring for multiple children. Referred spouses were significantly more likely to respond to the Millennium Cohort Family Study survey than those who were directly contacted; however, the overall success rate of using a referral strategy was less than recruiting spouses through direct contact. Differences between referred spouses and spouses contacted directly mirrored service member referring characteristics.

Depression among military spouses: Demographic, military, and service member psychological health risk factors.

BACKGROUND: More than a decade of war has strained service members and their families and the psychological health of military spouses is a concern. This study uses data from the largest study of military families in the United States to examine the demographic, military-specific, and service member mental health correlates of probable diagnosis of major depressive disorder (MDD) among military spouses. METHODS: Data were from service member-spouse dyads from all branches of the U.S. military. Demographic and military-specific factors were assessed using administrative personnel records and survey data. RESULTS: Of the 9,038 spouses, 4.9% had a probable diagnosis of MDD. In unadjusted models, spouses of service members who deployed and experiencecd combat-related events, were enlisted, had a probable posttraumatic stress disorder (PTSD) diagnosis, or screened positive for alcohol misuse were more likely to screen positive for MDD. In adjusted models, only spouses married to enlisted service members or those with PTSD had increased risk for MDD. Other demographic and military-related factors associated with MDD in spouses included less educational attainment, unemployment, having four or more children, and having prior military service (although not currently serving in the military) in the adjusted models. CONCLUSIONS: Findings characterize demographic, military, and service member psychological health factors that are associated with depression among military spouses. These findings imply that deployment alone may not negatively affect military spouses, but rather it may be the mental health impact on the service member, especially PTSD that increases the odds for MDD among military spouses.

Updated Global Estimates of Respiratory Mortality in Adults ≥30Years of Age Attributable to Long-Term Ozone Exposure.

BACKGROUND: Relative risk estimates for long-term ozone (O3) exposure and respiratory mortality from the American Cancer Society Cancer Prevention Study II (ACS CPS-II) cohort have been used to estimate global O3-attributable mortality in adults. Updated relative risk estimates are now available for the same cohort based on an expanded study population with longer follow-up. OBJECTIVES: We estimated the global burden and spatial distribution of respiratory mortality attributable to long-term O3 exposure in adults ≥30y of age using updated effect estimates from the ACS CPS-II cohort. METHODS: We used GEOS-Chem simulations (2×2.5º grid resolution) to estimate annual O3 exposures, and estimated total respiratory deaths in 2010 that were attributable to long-term annual O3 exposure based on the updated relative risk estimates and minimum risk thresholds set at the minimum or fifth percentile of O3 exposure in the most recent CPS-II analysis. These estimates were compared with attributable mortality based on the earlier CPS-II analysis, using 6-mo average exposures and risk thresholds corresponding to the minimum or fifth percentile of O3 exposure in the earlier study population. RESULTS: We estimated 1.04-1.23 million respiratory deaths in adults attributable to O3 exposures using the updated relative risk estimate and exposure parameters, compared with 0.40-0.55 million respiratory deaths attributable to O3 exposures based on the earlier CPS-II risk estimate and parameters. Increases in estimated attributable mortality were larger in northern India, southeast China, and Pakistan than in Europe, eastern United States, and northeast China. CONCLUSIONS: These findings suggest that the potential magnitude of health benefits of air quality policies targeting O3, health co-benefits of climate mitigation policies, and health implications of climate change-driven changes in O3 concentrations, are larger than previously thought. https://doi.org/10.1289/EHP1390.

Impacts and mitigation of excess diesel-related NOx emissions in 11 major vehicle markets.

Vehicle emissions contribute to fine particulate matter (PM2.5) and tropospheric ozone air pollution, affecting human health, crop yields and climate worldwide. On-road diesel vehicles produce approximately 20 per cent of global anthropogenic emissions of nitrogen oxides (NOx), which are key PM2.5 and ozone precursors. Regulated NOx emission limits in leading markets have been progressively tightened, but current diesel vehicles emit far more NOx under real-world operating conditions than during laboratory certification testing. Here we show that across 11 markets, representing approximately 80 per cent of global diesel vehicle sales, nearly one-third of on-road heavy-duty diesel vehicle emissions and over half of on-road light-duty diesel vehicle emissions are in excess of certification limits. These excess emissions (totalling 4.6 million tons) are associated with about 38,000 PM2.5- and ozone-related premature deaths globally in 2015, including about 10 per cent of all ozone-related premature deaths in the 28 European Union member states. Heavy-duty vehicles are the dominant contributor to excess diesel NOx emissions and associated health impacts in almost all regions. Adopting and enforcing next-generation standards (more stringent than Euro 6/VI) could nearly eliminate real-world diesel-related NOx emissions in these markets, avoiding approximately 174,000 global PM2.5- and ozone-related premature deaths in 2040. Most of these benefits can be achieved by implementing Euro VI standards where they have not yet been adopted for heavy-duty vehicles.

Preterm birth associated with maternal fine particulate matter exposure: A global, regional and national assessment.

Reduction of preterm births (<37 completed weeks of gestation) would substantially reduce neonatal and infant mortality, and deleterious health effects in survivors. Maternal fine particulate matter (PM2.5) exposure has been identified as a possible risk factor contributing to preterm birth. The aim of this study was to produce the first estimates of ambient PM2.5-associated preterm births for 183 individual countries and globally. To do this, national, population-weighted, annual average ambient PM2.5 concentration, preterm birth rate and number of livebirths were combined to calculate the number of PM2.5-associated preterm births in 2010 for 183 countries. Uncertainty was quantified using Monte-Carlo simulations, and analyses were undertaken to investigate the sensitivity of PM2.5-associated preterm birth estimates to assumptions about the shape of the concentration-response function at low and high PM2.5 exposures, inclusion of provider-initiated preterm births, and exposure to indoor air pollution. Globally, in 2010, the number of PM2.5-associated preterm births was estimated as 2.7 million (1.8-3.5 million, 18% (12-24%) of total preterm births globally) with a low concentration cut-off (LCC) set at 10µgm-3, and 3.4 million (2.4-4.2 million, 23% (16-28%)) with a LCC of 4.3µgm-3. South and East Asia, North Africa/Middle East and West sub-Saharan Africa had the largest contribution to the global total, and the largest percentage of preterm births associated with PM2.5. Sensitivity analyses showed that PM2.5-associated preterm birth estimates were 24% lower when provider-initiated preterm births were excluded, 38-51% lower when risk was confined to the PM2.5 exposure range in the studies used to derive the effect estimate, and 56% lower when mothers who live in households that cook with solid fuels (and whose personal PM2.5 exposure is likely dominated by indoor air pollution) were excluded. The concentration-response function applied here derives from a meta-analysis of studies, most of which were conducted in the US and Europe, and its application to the areas of the world where we estimate the greatest effects on preterm births remains uncertain. Nevertheless, the substantial percentage of preterm births estimated to be associated with anthropogenic PM2.5 (18% (13%-24%) of total preterm births globally) indicates that reduction of maternal PM2.5 exposure through emission reduction strategies should be considered alongside mitigation of other risk factors associated with preterm births.

The contributions to long-term health-relevant particulate matter at the UK EMEP supersites between 2010 and 2013: Quantifying the mitigation challenge.

Human health burdens associated with long-term exposure to particulate matter (PM) are substantial. The metrics currently recommended by the World Health Organization for quantification of long-term health-relevant PM are the annual average PM10 and PM2.5 mass concentrations, with no low concentration threshold. However, within an annual average, there is substantial variation in the composition of PM associated with different sources. To inform effective mitigation strategies, therefore, it is necessary to quantify the conditions that contribute to annual average PM10 and PM2.5 (rather than just short-term episodic concentrations). PM10, PM2.5, and speciated water-soluble inorganic, carbonaceous, heavy metal and polycyclic aromatic hydrocarbon components are concurrently measured at the two UK European Monitoring and Evaluation Programme (EMEP) 'supersites' at Harwell (SE England) and Auchencorth Moss (SE Scotland). In this work, statistical analyses of these measurements are integrated with air-mass back trajectory data to characterise the 'chemical climate' associated with the long-term health-relevant PM metrics at these sites. Specifically, the contributions from different PM concentrations, months, components and geographic regions are detailed. The analyses at these sites provide policy-relevant conclusions on mitigation of (i) long-term health-relevant PM in the spatial domain for which these sites are representative, and (ii) the contribution of regional background PM to long-term health-relevant PM. At Harwell the mean (±1 sd) 2010-2013 annual average concentrations were PM10=16.4±1.4µgm(-3) and PM2.5=11.9±1.1µgm(-3) and at Auchencorth PM10=7.4±0.4µgm(-3) and PM2.5=4.1±0.2µgm(-3). The chemical climate state at each site showed that frequent, moderate hourly PM10 and PM2.5 concentrations (defined as approximately 5-15µgm(-3) for PM10 and PM2.5 at Harwell and 5-10µgm(-3) for PM10 at Auchencorth) determined the magnitude of annual average PM10 and PM2.5 to a greater extent than the relatively infrequent high, episodic PM10 and PM2.5 concentrations. These moderate PM10 and PM2.5 concentrations were derived across the range of chemical components, seasons and air-mass pathways, in contrast to the highest PM concentrations which tended to associate with specific conditions. For example, the largest contribution to moderate PM10 and PM2.5 concentrations - the secondary inorganic aerosol components, specifically NO3(-) - were accumulated during the arrival of trajectories traversing the spectrum of marine, UK, and continental Europe areas. Mitigation of the long-term health-relevant PM impact in the regions characterised by these two sites requires multilateral action, across species (and hence source sectors), both nationally and internationally; there is no dominant determinant of the long-term PM metrics to target.

Rapid high-throughput characterisation, classification and selection of recombinant mammalian cell line phenotypes using intact cell MALDI-ToF mass spectrometry fingerprinting and PLS-DA modelling.

Despite many advances in the generation of high producing recombinant mammalian cell lines over the last few decades, cell line selection and development is often slowed by the inability to predict a cell line's phenotypic characteristics (e.g. growth or recombinant protein productivity) at larger scale (large volume bioreactors) using data from early cell line construction at small culture scale. Here we describe the development of an intact cell MALDI-ToF mass spectrometry fingerprinting method for mammalian cells early in the cell line construction process whereby the resulting mass spectrometry data are used to predict the phenotype of mammalian cell lines at larger culture scale using a Partial Least Squares Discriminant Analysis (PLS-DA) model. Using MALDI-ToF mass spectrometry, a library of mass spectrometry fingerprints was generated for individual cell lines at the 96 deep well plate stage of cell line development. The growth and productivity of these cell lines were evaluated in a 10L bioreactor model of Lonza's large-scale (up to 20,000L) fed-batch cell culture processes. Using the mass spectrometry information at the 96 deep well plate stage and phenotype information at the 10L bioreactor scale a PLS-DA model was developed to predict the productivity of unknown cell lines at the 10L scale based upon their MALDI-ToF fingerprint at the 96 deep well plate scale. This approach provides the basis for the very early prediction of cell lines' performance in cGMP manufacturing-scale bioreactors and the foundation for methods and models for predicting other mammalian cell phenotypes from rapid, intact-cell mass spectrometry based measurements.

MALDI-ToF mass spectrometry coupled with multivariate pattern recognition analysis for the rapid biomarker profiling of Escherichia coli in different growth phases.

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-ToF MS) has been exploited extensively in the field of microbiology for the characterisation of bacterial species, the detection of biomarkers for early disease diagnosis and bacterial identification. Here, the multivariate data analysis technique of partial least squares-discriminant analysis (PLS-DA) was applied to 'intact cell' MALDI-ToF MS data obtained from Escherichia coli cell samples to determine if such an approach could be used to distinguish between, and characterise, different growth phases. PLS-DA is a technique that has the potential to extract systematic variation from large and noisy data sets by identifying a lower-dimensional subspace that contains latent information. The application of PLS-DA to the MALDI-ToF data obtained from cells at different stages of growth resulted in the successful classification of the samples according to the growth phase of the bacteria cultures. A further outcome of the analysis was that it was possible to identify the mass-to-charge (m/z) ratio peaks or ion signals that contributed to the classification of the samples. The Swiss-Prot/TrEMBL database and primary literature were then used to provisionally assign a small number of these m/z ion signals to proteins, and these tentative assignments revealed that the major contributors from the exponential phase were ribosomal proteins. Additional assignments were possible for the stationary phase and the decline phase cultures where the proteins identified were consistent with previously observed biological interpretation. In summary, the results show that MALDI-ToF MS, PLS-DA and a protein database search can be used in combination to discriminate between 'intact cell' E. coli cell samples in different growth phases and thus could potentially be used as a tool in process development in the bioprocessing industry to enhance cell growth and cell engineering strategies.

Forecasting for fermentation operational decision making.

An awareness of the likely future behavior of a batch or a fed-batch fermentation process is valuable information that can be exploited to improve product consistency and maximize profitability. For example, by making operational policy changes in a feedforward control sense, improved consistency can be facilitated, while prior knowledge of batch productivity, or the end time, can help determine the downstream processing configuration and upstream process scheduling. In this article, forecasting methods based on multivariate batch statistical data analysis procedures are contrasted with case-based reasoning (CBR). Additionally, the importance of appropriate statistical data prescreening and the choice of a suitable metric for case selection are investigated. Two industrial case studies are considered, a fed-batch pharmaceutical fermentation and a batch beer fermentation process. It is demonstrated that, following appropriate statistical prescreening of the data, in terms of forecasting performance, CBR is comparable to linear projection to latent structures (PLS), for the more straightforward problem, i.e., the batch beer fermentation, while for the more complex case-the pharmaceutical process-CBR exhibits enhanced performance over PLS.

Impact of heavy metals on enzymatic activity of substrate and on composting worms Eisenia fetida.

The effect of heavy metals Cu and Zn on dehydrogenase and protease activity of the substrate during vermicomposting was investigated. Three dosages of Cu and Zn were tested in mesocosm experiments to investigate their bioaccumulation and impact on the reproduction of Eisenia fetida. Cu accumulated within the worm tissues in dosage concentrations up to a maximum level of 213 mg Cu kg(-1). The number of juveniles decreased from the lowest to highest dosages of Cu and Zn after 10 weeks of the experiment. Dehydrogenase showed a strong negative correlation (P < 0.01) with increased dosage of Cu, while protease remained unaffected. An overall reduction on dehydrogenase activity with increasing dosages of Cu and Zn indicated that these metals would impact detrimentally on the soil microbiology and consequently the stabilisation of the dosed media.