Author
ABSTRACT
Termed as the ‘main environmental cause of premature death’, Air Quality (AQ) Impact Assessment is crucial in today’s world. However the current lack of adequate AQ Impact Assessment in the United Kingdom has repeatedly been emphasized in previous studies to be one of the main factors in the reduced quality of Environmental Impact Statements (EISs). This research critically evaluates AQ impact prediction methods in three EISs through analyzing four stages: impact identification, impact assessment, significance evaluation and mitigation measures using a best practice technique evaluative checklist approach. Systemic strengths identified were comprehensive impact identification and qualitative significance evaluation whilst systemic weaknesses of satisfactory impact assessment due to incomplete data collection and poor mitigation measures discounting post-monitoring practices were recognized. Research is exclusive to National Significant Infrastructure Projects (NSIPs), representing a small percentage of EIS categories in the United Kingdom.
1. INTRODUCTION
1.1 ENVIRONMENTAL IMPACT ASSESSMENT (EIA).
EIA is defined by Glasson and Therivel (2019, p. 4) as ‘a process, a systematic process that examines the environmental consequences of development actions, in advance’; emphasizing on it being a process in relation to the steps to be followed. It is intertwined with all relevant environmental science pathways (Figure 1), indicative of its significance and called a ‘precautionary principle’ (Glasson et al., 1997).
Figure 1: Interlinkage with environmental science pathways (Adopted from Bond, 2019).
First established through the National Environmental Policy Act (NEPA) in the United States in 1969 (Glasson et al., 2005; Greenburg, 2010, p. 2), it has now transcended worldwide, albeit in different capacities as different methods (Pope et al., 2013). Governed by 29 specific legislation acts in UK and EU legislation (Glasson and Therival, 2019, p. 65), its applied to all Annex I and II projects (EU Directives) and Schedule 1 and 2 projects (Town and Country Planning Regulations, 2017), respectively.
EIA, although illustrated as a linear process (Figure 2), is anything but. All steps are interlinked, displaying the cyclic complexity of the process (Glasson and Therivel, 2019, p. 4). It starts with project screening and scoping; identifying the need for an EIA and early Impact Assessment (IA) with alternatives, respectively. Often termed as the most important step (Glasson and Therivel, 2019, p. 5), the IA section is divided into sub-categories. An Environmental Impact Statement (EIS) is then presented for review to relevant decision-makers. Post-monitoring of mitigation strategies and consequent audits are very necessary.
Figure 2: EIA Process (Glasson, 1999).
1.2 ENVIRONMENTAL IMPACT STATEMENT (EIS).
An EIS is a “legal tool that ensures that major federally initiated projects and new or substantially modified programs undergo a comprehensive review before committing major resources and beginning construction or implementation…leap” (Greenburg, 2010, p. 4).
It condenses the EIA process into a single entity; presenting the decision maker with relevant information on the proposed development, its consequent environmental impacts and mitigation methods required for potential adverse effects (Peterson, 2010). It is a comprehensive statement to provide relevant authorities accurate information for decision-making (Fernandez et al., 2018).
Introduced as a mandatory requirement under the EIA Directive 85/337/EEC, which amended to Directive 97/11/EC; further consolidated to 2011/92/EU; which was amended to EIA Directive 2014/52/EU; the most recent EIS legislation to be translated into English law. It is implemented in England inter alia through the Part 5 of regulation 18 of the Town and Country Planning Regulations 2017, which extensively states the overall scope of the statement.
However EIS quality has recently become a subject of debate. Despite increased literature reviews, Wood (2008) points out the failure to include detailed analysis on impact prediction (IP) quality; which according to Badr et al., (2004), could improve feedback to boost effective IP practices. This is further agreed to by a study by Peterson (2010) that assessed randomly selected 50 EISs on the quality of its IP section and found that more than 50% barely passed the satisfactory threshold.
The above highlights the need for continuous improvement needed in EIA and EIS quality, through improvements especially focused on the stages in impact prediction.
1.3 IMPACT PREDICTION.
Impact prediction (IP) is one of the key areas in an EIS (Sadler, 1996; Kabir and Momtaz, 2012) that consequently determines its quality (Glasson et al., 1997) though subjective due to the many factors at play (Fuller, 1999). It consists of four main stages:
Stage 1: Impact Identification – the recognition of objective changes; anticipated to be caused by the proposed project through all life stages compared to the absence of the project. Different techniques are employed dependent on the project-type (Morrison-Saunders and Bailey, 2003).
Stage 2: Impact Assessment – determining the magnitude of recognized impacts i.e. its variance from the established baseline conditions, to be quantified where possible, which will affect the project design.
Stage 3: Significance Evaluation – determining the weight and value of identified impacts anticipated in having adverse changes in the environment. Factors include effect intensity, effect duration and degree of certainty/likelihood of predicted impacts (Scottish National Heritage, 2013).
Stage 4: Mitigation measures – determination of moderation measures to reduce/prevent identified anticipation adverse impacts. As stated by Scottish National Heritage (2013), it should include avoidance, cancellation, reduction and compensatory measures.
There is paramount importance of IP in determining EIS quality as it ensures, where possible, correct and accurate information to be made available to relevant authority for ‘the evaluation of significance of impacts’ (Schmidt et al., 2008). Uncertainties, hidden to decision makers, caused by limited information, may lead to compromising integrity of the decision made (Tenney et al., 2006). Glasson et al., (1997) describe IP to be an important quality determinant for an EIS; backed by Ortolano, (1993). As described by Fuller, (1999), impact prediction documentation is mindfully related to the overall EIA and hence EIS quality.
However, a comprehensive study by Barker and Wood (1999), backed by Morrison-Saunders (2001); highlighted IP as the least well performing area in EISs across eight EU nations; key reasons identified included ‘limited details of scoping methods’, lack of detailed impact prediction and evaluation methodology. Additionally research on EIS quality by Kabir and Momtaz, (2012) also supported the aforementioned findings. Identified reasons included lack of detailed information, lack of clear process description and evaluation criteria, lack of satisfactory public involvement and lack of decision justification amongst others.
The verdict of a poor-performing IP section and its repeated reasons is resonated in other studies (Wood et al., 1996; Cashmore et al., 2002; Morrison-Saunders and Bailey, 2003; Ross et al., 2006; Sandham et al., 2008), a list not exhaustive. This alarmingly brings to attention the need for further research and refinement in the IP process to determine Best Practice Techniques (BPT) that will better EIA and consequent EIS quality.
Hence, this research uses Air Quality (AQ) as the chosen environmental component to compare BPT (focused on four stages) for IP against three published EISs.
1.4 SETTING THE SCENE: AIR QUALITY (AQ).
AQ is determined by assessing air pollutants, dust and odour concentrations and magnitude in a given environment (Therivel and Wood, 2018). The World Health Organization (1999) recognizes AQ as one of the most important environmental components due to its extent of exposure and composition, to human and ecological health. Termed the ‘main environmental cause of premature death’ (Kelly and Fussell, 2015; OECD, 2014), it highlights strong potential health impacts due to correlated pollutant toxicity e.g. Nitrogen Oxide, PM2.5, PM10 amongst others. This is further supported by Harrop (2002) who strongly links impact severity with pollutant concentration, mixture, length of exposure and sensitivity of the receptor. Research by Künzli et al., (2000) estimates air pollution from car emissions causes two-fold deaths in comparison to car crashes. Furthermore its fluidity highlights its inability to be bound to confined areas, leading to adverse large-scale health impacts (IAQM, 2017).
Being of an important nature, there is a significant legislation focused primarily on AQ (AQEG, 2012). The primary EU legislation (DEFRA, 2019) is the Ambient Air Quality Directive (2008/50/EC), the National Emissions Ceilings (NEC) Directive (2016/2284/EU) and the Fourth Daughter Directive (2004/107/EC); in the UK, and the Clean Air Act (1993) and Air Quality Regulations (2010) amongst others, respectively.
Additionally, assessing AQ as a single environmental component (Figure 1) for IA has been rarely done (Badr et al., 2004). Research supported this through Brusselsen et al., (2016), who measured health impacts of a ‘filtered tunnel ring road’ and concluded that poor AQ impact prediction may have produced compromised results and hence the urgent need for improvement. Assessment by IAQM (2019, p. 6), highlights specific problems in AQ impact prediction; varying from lack of detailed process approach, criteria to be scanned in, collaboration between different experts and professional judgment of results for impactful analysis.
Despite AQ’s illustrated ingrained importance in the environment, capable of adverse effects, it still struggles to be effectively assessed. This highlights a research gap that is critical to AQIP in regards to the widespread nature of this component; which this study aims to acknowledge.
1.5 AIMS AND OBJECTIVES.
The aim is to critically evaluate the four key stages IP in the EIA procedure by sampling three EISs; using AQ via a criteria-based BPT review checklist. This will also address the lack of research in AQ Impact Assessment. The aim can be met using four objectives outlined below:
– To institute a BPT for impact prediction quality by reviewing extensive literature of available methodologies and AQ legislation.
– To develop a BPT review methodology to subject on each of the four IP components.
– To apply the BPT across all IP components in the three EISs (focused on AQ) for critical evaluation of the IA methods.
– To use the findings as a precursor to recommending developments for aiding practitioners in future IP practices and to an extent, AQ research.
2. METHODOLOGY
2.1 SAMPLES STRATEGY.
The three EISs used for analysis are:
- Great Yarmouth Third River Crossing (Norfolk County Council, 2019).
- VPI Immingham Open Cycle Gas Turbine (OCGT) Project (AECOM, 2019).
- North London (electricity line) reinforcement (AMEC, 2012).
It is noted that this is a restricted sample; all are National Strategic Infrastructure Projects (NSIP’s); subject to the EIA process by the National Infrastructure Directorate of the Planning Inspectorate (Localism Act 2011); the scoping opinion determined by the Minister of State. Governed under Town and Country Planning Regulations 2017; primary legislation for NSIPs is the Planning Act 2008 (amended by Marine and Coastal Act 2009, Localism Act 2011, Growth and Infrastructure Act 2013, Infrastructure Act 2015, Housing and Planning Act 2016 and Wales Act 2017); and national policy statements (The Planning Inspectorate, 2012).
However they miss representing the regional or local county or unitary EIS sector; which may lead to important information being forfeited in the evaluative criteria (Wang, 2013); and inconsideration of other variables such as length, available funds, preparation time amongst others (Morrison-Saunders et al., 2001; Joseph et al., 2015).
Nonetheless the sample fits in The Infrastructure Planning (Environmental Impact Assessment) Regulation 2017 (Schedule3, Para 3; Schedule 4). Despite each project being in a different industry sector, it can be considered to miss out on representation of the overall six NSIP sector types (The Planning Inspectorate, 2012). However consistency and quality assessment is aimed to be attained using only NSIP samples; hence the critical evaluation of AQ practices will be specific to this level of project-type.
2.2 BEST PRACTICE TECHNIQUE (BPT): AQIP.
Best practice technique is balanced methodology with a consistent approach to all assessment sections; a merger between extensive literature review and author acumen (Joseph et al., 2015). Therefore its reproducibility is paramount (Simpson, 2001), being fully abiding of all relevant practices and legislation.
Criteria-based approaches have been successfully used in many studies (Landim and Sánchez, 2012, p. 218; Thompson, 1990; Lee and Brown, 1992; Cashmore et al., 2004; Canelas et al., 2005; Glasson et al., 2005; Tinker et al., 2005); and credible studies and organizations (IAIA, 2006; IEMA, 2016) basing their criteria derived from the Lee et al., (1992) EIA Review Package and IAU Oxford Brookes University EIS Review Package (Glasson and Therivel, 2012). Continuous use of this method signifies replicability and success in ensuring fair IP assessment by covering all legislation and current practice in the criteria (European Commission, 1999).
However this method may contain obstacles e.g. missed interactions of indirect impacts and bias if lone reviewed (European Commission, 1999).
Additionally, the above detailed assessment technique, specific to AQ, has not been broadly applied to AQIP. Research by Ramli et al., (2004) reviewed 50 EISs looking at AQIP and concluded that 56% of the EISs lacked detailed quality assessment.
With the aim of critical evaluation, this research assesses AQ IP practices in 3 EISs using a criteria-based checklist (BPT), using review grades (Lee et al., (1992) from A to E:
Each criterion is graded for each EIS and given an average mark. The average marks are then combined for the stage and an overall mark for the whole stage is determined.
The BPT checklist (Table 1) used is further outlined in the results section.
3. RESULTS
The following checklist (Table 1) was used to critically evaluate air quality assessment in the three EISs:
Table 1: Evaluative Checklist.
NO. | EVALUATION CRITERIA | EIS | AVERAGEGRADE | ||
1 | 2 | 3 | |||
1 | IMPACT IDENTIFICATION | ||||
1.1 | Comprehensive project description (IAQM, 2009) with:
1. Identifying current baseline Air Quality (AQ) conditions using appropriate systematic methods e.g. current air emissions data. 2. Identifying future baseline conditions through e.g. numerical dispersion models (Gaussian Air Model), line and point source models, local and regional dispersion scale models, expert opinions and/or any other approved method. 3. Declaration of local Air Quality Management Area(s) (AQMA) if applicable – consideration of area sensitivity (ecological) and land use. | A | A | A | A |
1.2 | Concise description and reasoning for use of systematic impact prediction (IP) methods e.g. project-specific checklists, matrices, expert consultations; model verification, identified pollutant thresholds (Infrastructure Planning Regulations, 2017; Clean Air Strategy, 2019) including:
1. Equal consideration and use of at least three quantitative (e.g. air pollution dispersion model) and qualitative (e.g. interviews with locals) identification methods. 2. Inclusion of all overlying legislation (regional, local and national) e.g. Planning Policy Acts (PAN 51, 2006; PPS23, 2004; Policy Planning Wales, 2010; LAQM PG16, 2016) and permits where relevant. | A | B | B | B |
1.3 | Identification focused on the following with specified time periods (EPUK, 2010; Therivel and Wood, 2018):
1. Meteorological data – wind speed and direction for e.g. 2-5 years prior and post. 2. Traffic analysis – 1-3 years prior and post. 3. Emissions standards – specific on-site pollution and pollutant sources (Critical levels and Critical loads) – Current and 1-3 years post. 4. Spatial Planning – Air sensitive receptor (ASR) locations in regards to nearby pollution sources – 1-3 years prior and post. 5. Embedded and Additional effects – Current and 1-3 years post. | A | A | A | A |
1.4 | Quantified identification with correct units of pollutants and particulate emissions (Ambient Air Quality, 2008/50/EC; Clean Air Strategy, 2019) during all phases of the project, where applicable: 1. Fine Particulate Matter (PM2.5) 2. Sulphur dioxide (SO2) 3. Ammonia (NH3) 5. Non-methane volatile organic compounds (NMVOCs)* 7. Cadmium (Cd) 8. Lead (Pb) 9. Benzene 10. Nitrous dioxide (NO2) 11. Arsenic (As) 12. Nickel (Ni) 13. Ozone (O3) 14. Fine Particulate Matter (PM10) 15. 1,3 Butadiene 16. Benzo(a)pyrene (B(a)P). | B | A | A | A |
1.5 | Investigation and description of effects on specific receptors and any other relevant receptors (Mohamed, 2009) including:
| C | B | C | C |
1.6 | Impact identification from non-standard operating procedures, e.g. accidents, emergencies (Mohamed, 2009; EPUK, 2010):
| C | B | B | B |
1.7 | Identifying toxicity (compositions) and emission thresholds from other hazards (IAQM, 2009). | C | B | B | C |
1.8 | Information collection through primary and secondary sources (Mohamed, 2009; EPUK, 2010):
1. Conducting a pilot site visit – carrying out field survey for onsite monitoring, preliminary identifications of potential impacts and planning purposes. 2. Secondary legislation and reports from various sources – desk-study. | A | D | B | C |
1.9 | Identifying possible trans-boundary impacts – use of collaborative expertise methods (Therivel and Wood, 2018). | B | C | A | B |
2 | IMPACT ASSESSMENT | ||||
2.1 | Separate impact assessment of control conditions vs. with the project (EPUK, 2010; IAQM, 2009; Clean Air Act, 1993):
1. Comprehensive comparisons for all project stages against all receptors. Individual impact determination from deviations via the current AQ baseline conditions. 2. Description of assessment methods and justification in regards to individual impact intensity and proximity e.g. use of concentration contours on project site maps for air pollution impacts (Therivel and Wood, 2018). Considerations of all quantitative and qualitative methods and compositions; degree of project-specificity and determination of current and future impacts. | A | A | A | A |
2.2 | Justification of collected data suitability considering the development scale and impact dispersion complexity (EPUK, 2010; DEFRA, 2019). | B | A | B | B |
2.3 | Discussion of areas where assessment could not be carried out and justification for the same (Mohamed, 2009). | C | C | C | C |
2.4 | Trans-boundary impact assessment – justified use of collaborative expertise (Therivel and Wood, 2018). | B | C | C | C |
2.5 | Concise future impact predictions with defined time periods (IAQM, 2009; EPUK, 2010) e.g. pollutant impacts over the next year or 3 years. | A | B | B | B |
2.6 | Difficulties, uncertainties and constraints in information collation explained (IAQM, 2009). | C | D | D | D |
3 | SIGNIFICANCE EVALUATION | ||||
3.1 | Clear identification and description of significance evaluation tools e.g. matrices, checklists, and local maps among others, used on each project phase linked to each receptor (IAQM, 2009; EPUK, 2010; IEMA, 2016). | B | A | B | B |
3.2 | Concise discussion of impact sensitivity, intensity and proximity to all affected receptors over a specific time frame e.g. impacts of annual individual pollutant concentrations on local ecology (IAQM, 2009; EPUK, 2010; IEMA, 2016). | A | B | A | A |
3.3 | Use of evaluative practices for a clear distinction between high and low significant impacts respectively (IAQM, 2009; EPUK, 2010; IEMA, 2016):
1. Further differentiating the significance of impacts present without the project and impacts to be caused by the proposed project with justified certainty. 2. Identification of AQ impact frequency. 3. Use of secondary data for evaluation – e.g. using significance evaluation information from similar projects within a given radius. | A | B | B | B |
3.4 | Consideration and evaluation of deviations from current AQ baselines (IAQM, 2009; EPUK, 2010; IEMA, 2016) in assessing significance with:
1. The possibility of breaches e.g. in emission thresholds. 2. Justification of all deviations and breaches – e.g. use of valid modeling methods e.g. air dispersion models, point and line source models and/or other approved methods. | A | A | A | A |
3.5 | Consideration of all expertise used and compliance to all relevant primary and secondary legislation e.g. The Clean Air Act (1993). | A | A | A | A |
3.6 | Performance comparison e.g. against the national, regional and local AQ standards, if applicable (EPUK, 2010). | A | A | D | B |
3.7 | Quantification of potential individual changes with comprehensive explanations – clear line of analysis seen from magnitude and significance evaluation to explanations of the findings (IAQM, 2009; EPUK, 2010). | A | A | A | A |
3.8 | Special incorporation of significance evaluation and impact magnitude on an identified AQMA. Justification of results through the method (Therivel and Wood, 2018). | C | A | A | B |
3.9 | Comprehensive discussion of identified cumulative and residual impacts (EPUK, 2010). | A | A | A | A |
4 | MITIGATION MEASURES | ||||
4.1 | Identification and implementation of project-specific mitigation measures for all identified significant adverse impacts on AQ (IAQM, 2009; EPUK, 2010; IEMA, 2016):
1. Consistent to the project type for all individual phases: a. Construction (general, dust and odours, traffic and pollutant emissions). Possible application of a Construction Environmental Management Plan (CEMP). b. Operation (transport, non-transport, building design). 2. Appropriate and specific to all individual point sources e.g. traffic, pathways and receptors e.g. local ecology. 3. Considering all cumulative and residual effects. | A | B | C | B |
4.2 | Consideration of the effectiveness of embedded and additional project-specific mitigation methods (IAQM, 2009; EPUK, 2010; IEMA, 2016) including:
1. Justification of mitigation measures for significant impacts (proportionality to each) e.g. Low Emissions Strategy (DEFRA), creation of buffer zones. Scale of impact needs to be considered. 2. Justification of mitigation measures for unclear/uncertain impacts. | A | B | C | B |
4.3 | Clear and concise mitigation schedule – outlining periodic mitigation implementation process timeframe especially including post-decision monitoring and auditing (EPUK, 2010). | C | E | E | E |
4.4 | Clear and concise consideration to pattern changes and deviations from baseline impacts (IAQM, 2009; EPUK, 2010; IEMA, 2016) and relevant justification. | A | A | D | C |
4.5 | Clear consideration of individual mitigation method uncertainties, limitations and shortcomings (EPUK, 2010; IEMA, 2016):
1. Identification and justification of each. 2. Consideration on the consequent impacts on mitigation methods effectiveness. 3. Possible solutions for each for future practices. | D | C | E | D |
4.6 | Mitigation method-type: clear cost-benefit analysis (IAQM, 2009; EPUK, 2010; IEMA, 2016; Therivel and Wood, 2018). | E | E | E | E |
4.7 | Evidence of compliance demonstration with air emission limit values and legislation e.g. pollution and prevention control (PPC) permits (DEFRA, n.d.). | A | A | A | A |
4. DISCUSSION
The cyclic interlinked nature of EIA is applicable to impact prediction as the quality of the first stage impacts the following stages, evident in this critical evaluation carried out. This research determines a number of systemic strengths and weaknesses.
Impact identification was a well-performed stage with comprehensiveness and compliance to all relevant legislation. However only operational impacts were primarily focused on, discounting any impacts from non-operational conditions. Impact assessment saw detailed inclusion of all relevant qualitative and quantitative methods and overall suitability of collated data but missed out on any uncertainties involved in data collation (from incomplete data collection); which possibly led to a compromise in qualitative assessment (Tenney et al., 2006).
Significance evaluation was the most thoroughly carried out stage; highly qualitative and quantitative; excellent use of appropriate magnitude and significance tools for each identified impact. Contrarily mitigation measures were the poorest quality stage; most criterion were evaluated as below par, between just satisfactorily and poorly performed whilst some being completely overlooked.
Appreciative of the strengths, there is room for improvement. As observed in earlier studies (Morrison-Saunders, 2001; Badr et al., 2004; Joseph et al., 2015) and this research, impact prediction systemic weaknesses lay in incomplete data collection and mitigation measures with future practices. Despite focus on just impact prediction and assessing air quality as the only environmental component, the weaknesses are seen across various studies assessing full EISs. This signifies the need to ensure correct data collection for all impact identification and importance on mitigation measures especially post-monitoring practices.
It is to be remembered that this research is only relevant to NSIPs, which represent the tiniest percentage of all EISs and do not include local and regional legislation hence may have missed out on other important information, which could give rise to different systemic weaknesses and strengths. Hence analysis of a wider sample inclusive of all types of EISs and legislation is needed for better and improved research.
5. CONCLUSION
This research is restricted to identifying systemic strengths and weakness in a very small sample of three EISs, focused air quality impact prediction in NSIPs; unique to an organization carrying out scoping instead of the local authority. This was done to gain consistency in results, which is hence observed and presumed to be of equal quality. However the lack of including all other EIS categories mean missing out on assessing quality variances between the Local Planning Authority and the National Planning Inspectorate.
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