Not bugging the neighbours: building an evidence-based regulatory - PowerPoint PPT Presentation

Not bugging the neighbours: building an evidence-based regulatory framework for industrial bioaerosol management Rob Kinnersley Kerry Walsh Research, Analysis and Evaluation

The drivers Increased diversion of organic waste from landfill to composting, AD, MBT Intensive farming as a means of rural economic growth Public and professional concerns over possible health impacts from process contribution to bioaerosol exposure

Are people near biowaste and intensive farming sites at increased risk of adverse health outcomes through exposure to additional bioaerosol? • Clinical/Epidemiological evidence for harm from bioaerosol • Standardised methods for bioaerosol monitoring to allow intercomparison • Sufficient standardised data to give reliable exposure estimates What types and concentrations of additional bioaerosol might people be exposed to? Are such process contributions likely to have an adverse impact on human health? YES NO

Are such process contributions likely to have an adverse impact on human health? YES NO  Regulatory approach to protect human health  Means of assessing impact of proposed new sites/expansion  Performance of mitigation measures  Consistent means of monitoring site performance at proportionate cost

Impacts 1

Impacts 1  Impacts at high concentrations  Insufficient data or mechanistic understanding for lower cut-off Precautionary position

Sampling - objectives Collect consistent bioaerosol concentration and speciation data, with specified uncertainties Research for use in epidemiology and clinical research and defining management practices Regulatory to set and enforce a proportionate, targeted and enforceable regulatory position

Sampling - scenarios

Sampling - scenarios Media Sample time Extraction

Sampling - scenarios Sample times Solutions

Sampling - scenarios

Sampling - scenarios Work in Progress…

Exposure - waste

Exposure – intensive agriculture

Exposure – intensive agriculture

Mitigation Distance Containment Stacks “Tech”

Mitigation Biofilter/scrubber performance 8 sites with open or closed biofilters, some with acid scrubbers Performance variable between filters and replicates Emission concentration independent of input concentration Removal of total bacteria, gram negatives and Af not correlated. No clear link between operating conditions and efficiency

Impacts 2 – this time it’s causal Next-generation Sequencing (Illumina MiSeq) of bioaerosol samples from intensive farming facilities Samples were collected using pre-sterilised IOM personal sampling heads operated as per the AfOR (2009) protocol – not ideal but linked into agreed sampling plan. • Fungal primers: ITS2_KYO1 and ITS1R_Wobble (CWGYGTTCTTCATCGATG) amended version of ITS2 (White et al. 1990). • Bacterial primers: 16S 515F and 806rB from Earth Microbiome project (Caporaso et. Al. 2012). • Bash scripts used to complete chimera checking, OTU picking, taxonomy assignment and relative abundance calculations can be found at http://www.github.com/rachelglover/bioaerosol

Fungal bioaerosol profile from intensive farming facilities: Family level Percentage of overall reads attributed to source 35 30 25 20 15 10 5 0 Broiler Layer Swine No Taxa assigned identity Broilers: 81 Layers: 101 Swine: 156

Most common taxa attributed to source: Fungi Relative abundance Taxa Identity (% reads in DW sample attributed to source) Broilers 23.5 Aspergillus cibarius 12 Rhodosporidium diobovatum 11 Malassezia sp. 6.3 Penicillium solitum Layer 32 Bjerkandera adusta 20 Scutellinia scutellata 7.4 Penicillium brevicompactum 4.3 Aspergillus cibarius Swine 11 Sarocladium strictum 10 Cryptococcus victoriae 6.5 Unidentified Lasiosphaeriaceae 6.3 Harzia acremonioides

Bacterial bioaerosol profile from intensive farming facilities: Phyla level % overall reads attributed to source 50 40 30 20 10 0 Broiler Layer Swine No Taxa assigned identity Broilers: 228 Layers: 409 Swine: 562

Most common taxa attributed to source: Bacteria Relative abundance Taxa Identity (% reads in DW sample attributed to source) Broilers 7.7 Chryseobacterium sp. 4.2 Unidentified Gaiellaceae 4 Sediminibacterium sp. 3.5 Unidentified Bifidobacteriaceae Layer 5.7 Lactobacillus sp. 5.4 Knoellia 5 Faecalibacterium prausnitzii 4.9 Carnobacterium sp. 4.9 Unidentified Xanthomonadaceae Swine 6.4 Lactobacillus sp. 5.4 Unidentified Aerococcaceae 4.6 Unidentified Clostridiaceae 3.2 Prevotella sp.

Evidence gaps More monitoring to build understanding of the “exposure envelope” and variability in more situations, including background Low-cost continuous or periodic monitoring to improve spatiotemporal understanding and lower cost/increase efficacy of regulatory measurements Better understanding of CAUSES of impacts Refine monitoring targets e.g. Endotoxin? Specific DNA?