*3.5. Validation*

#### 3.5.1. WorldView-2 Based Validation

The classified WorldView-2 imagery and the AVIRIS-based MESMA cover fractions were compared using the 120 randomly selected polygons across the two image types (Table 6). The best linear correlations between WorldView-2 fractions and MESMA were observed for the GV cover, which displayed a near 1:1 relationship and had generally high r2 values regardless of the endmember selection technique used. Correlation coefficients for char were generally fairly high (between 0.59 and 0.741), however, all models showed a tendency to under-model char, as demonstrated by linear models consistently having an intercept above 0.2. Correlations were generally poor for NPV and soil, with r2 values often below 0.2. Larger endmember libraries did not always translate to higher fractional cover accuracy; for example, the highest r2 for the char fraction was found for the uSZU EMC library (0.741).

**Table 6.** The coefficient of determination (r2), intercept, and slope value of a linear fit between WorldView-2 estimated cover fractions as the dependent variable and AVIRIS derived MESMA cover fraction for different endmember and band selection techniques as the independent variable. Green vegetation is abbreviated as GV, non-photosynthetic vegetation as NPV.

Plots of the linear regression for the uSZU IES MESMA run and the WorldView-2 classifications are provided below (Figure 6). The plots are broadly representative of the trend of most MESMA versus WorldView-2 relationships. There is general support for the linear relationship of the GV identification by MESMA and the WorldView-2, with a high r2 value and little systematic error. Based on the manual interpretation of the WorldView-2 imagery, MESMA appears to be systematically modeling a lower fraction of NPV and char cover, and modeling a higher fraction of soil cover.

**Figure 6.** Scatter plots of the relationship between the 120 WorldView-2 classification points and MESMA with the IES endmember selection technique for green vegetation (GV), char, non-photosynthetic-vegetation (NPV), and soil.

#### 3.5.2. Comparison with GeoCBI Plot Data

Due to the similarity between MESMA unmixed models used in this study, we limit our comparison to only the cover fractions generated from MESMA using the IES library and the full AVIRIS bands. First, the relationship between individual cover fractions and GeoCBI was assessed (Figure 7). The relationship between the GV fraction and GeoCBI appeared to be inversely linear (a linear regression produced an intercept of 2.93, a slope of −1.89 and r2 value of 0.644). In contrast, the relationship between NPV and GeoCBI is clearly non-linear. NPV fractions are generally highest at GeoCBI values of 1.5 to 2.5 suggesting that NPV is high at moderate severities, but low at the highest and lower severity levels. The char fraction appears to be more of a binary relationship only appearing in the model at the highest GeoCBI levels (above 2.75) and is at or near zero for all other GeoCBI levels.

**Figure 7.** Scatter plots of the relationship between GeoCBI, a ground plot derived method of measuring fire severity and estimated cover fractions for green vegetation, non-photosynthetic vegetation, and char.

#### 3.5.3. Comparison with Field Tree Status Data

We also compared the MESMA cover fractions of GV, NPV, and char with the percentage of canopy level trees with mostly green, brown, and black needles (Figure 8). Similar to the GeoCBI values, the char percentage and percentage of black trees appeared to have a near binary relationship in the plots. If the percentage of field identified black trees within the plot was under 50%, then typically no char fraction was modeled for the pixel, if it was above 50%, then the char fraction was almost always modeled as close to 100%. The relationship between the brown trees and NPV appeared to generally be more linear, although the modeled NPV cover was never greater than 50% even with near 100% brown trees. The relationship between the green trees and green vegetation may be positively linear, but the amount of scattering makes interpretation difficult. It is important to remember that few AVIRIS pixels are purely tree cover, most are a mixture of tree and substrate, so a pixel with 100% brown trees may indeed be made up of 50% other materials. Finally, it is important to note that the percent of trees in the plot may not be an ideal reference for the cover fraction as it does not account for variation in tree density, and this may partially contribute to the observed biases.

**Figure 8.** Scatter plots of the relationship between percent of black, brown, and green trees, and estimated cover fractions for green vegetation, non-photosynthetic vegetation, and char.


*2.2. Recent Syntheses*

Thus, kainic acid raised great interest in synthetics due its activity, wide use in experimental pharmacology, and probability to synthesize analogs and derivatives which could be applicable in medicine to treat schizophrenia and other brain diseases. Taking into account that configurations of its three stereogenic centers are crucial in binding to receptors and functional activities of this compound, the stereoselective synthesis of **2** was directed first to the optically active form of **2**, identical to the natural product. Different synthetic strategies, based on C2–C3 or C3–C4 bond formations (the latter approach was used in majority of syntheses), C4–C5 bond formation and C-*N* bond formation pathways, as well as on the use of the existing pyrrolidine ring and cycloaddition reactions, were applied to synthesize this excitatory amino acid. As a result, about 40 total multi-step syntheses of **2** were reported in literature and reviewed in 2012 [19].

In our review, we discuss only some of syntheses reported later. Most of them are based on novel schemes. We omit listing all the stages of these syntheses here and, instead, pay attention to main ideas and results achieved in comparison with the already known approaches.

Poison et al. [20] elaborated an efficient synthesis of (–)-kainic acid (**2**) using just two of the above-mentioned approaches: application of a pyrrolidine precursor and cycloaddition. A high-pressure Diels-Alder cycloaddition of the obtained from 4-hydroxy-l-prolin (**9**) 3,4-unsaturated pyrrolidine derivative **10** with Danishefsky's diene at high pressure (15 kbar) and room temperature gave bicyclic product **11**. In this case, high pressure provided the gain in reactivity and led, after some additional treatment and 82 h of exposure, to a 96% conversion into **11** that contained *N*-protected trisubstituted pyrrolidine cycle. The subsequent decarboxylation and transformation of its six-membered ring to isopropenyl and carboxymethyl groups gave **12** converted into target product via pyrrolidine derivative **13** with almost total stereocontrol and an approximately 10% yield (Scheme 1). At the same time, the key intermediate product, enone **11**, and related compounds were partly racemic in their first synthesis attempts. However, preliminary purification of the initial Danishevsky's diene by rapid distillation and removal of trace triethylamine from it made it possible to synthesize **11** with excellent yield and high enantiomeric excess.

New approaches, based on the formation of C3–C4 bond and involving the Ireland-Claisen rearrangement of allylic esters, were used by Indian and Japanese groups. Reddy and Chandraseker [21] utilized this rearrangement along with the Sharpless asymmetric epoxidation. The first of these reactions was used to create C3 and C4 *cis* stereocenters, while the Sharpless oxidation allowed the designing of chirality at C2. Authors constructed the target compound 2 from ynone **14**. At the first stages, this compound was converted into alcohol **15** by Noyori reduction and Red-Al

transformation of triple bond into double one. Reaction of **15** with 3-methyl-3-butenoate catalyzed by *N*,*N*- -dicyclohexylcarbodiimide (DCC) and 4-dimethylaminopyridine (DMAP) gave the allylic ester **16**, which was converted into **17** by the Ireland-Claisen rearrangement with lithium bis(trimethylsilyl)amide (LiHMDS) and trimethylsilyl chloride (TMSCl). The subsequent four steps of transformations led to allyl alcohol **18** with removal of p-methoxybenzyloxy (PMBO) protective group. The obtained **18** was epoxidated by asymmetric Sharpless epoxidation using (–)-diethyltratrate (DET), titanium isopropoxide Ti(iOPr)4, and *tert*-butyl hydroperoxide (TBHP) to give **19**. Further conversion provided azide **20**. Reduction of **20** allowed the formation of pyrrolidine ring, and then NH in this ring was protected by reaction with *tert*-butyloxycarbonyl anhydryde ((tBoc)2O) to obtain **21** in several steps. Removal of the protective group and oxidation with Jones reagent (CrO3 in aqueous sulfuric acid) led to kainic acid (**2**) (Scheme 2). Thus, a new strategy for constructing the core system of **2** was realized in this synthesis.

**Scheme 1.** Synthesis of kainic acid by Diels-Alder reaction.

**Scheme 2.** A shortcut scheme of synthesis of kainic acid via chirality transfer through the Ireland-Claisen rearrangement.

A similar strategy, also based on Ireland-Claisen rearrangement, was proposed by Japanese scientists as a unified approach to synthesize not only kainic acid proper, but also bioactive 4-substituted kainoids [22]. A source compound **22** was obtained from l-tartaric acid through four steps. Condensation of 22 with 3-methyl-3-butenoic acid gave ester **23** which, through the Claisen-Ireland rearrangement with LHMDS in the presence of TMSCl, was converted with high diastereoselectivity into carbonic acid **24**. The subsequent reduction of a carboxy group and other transformations gave aminomethyl derivative **25**. The resulting product was cyclized exceptionally easily through the palladium-mediated pyrrolidine-ring formation into the product **26** that had the required stereochemistry for transformation into kainic acid or its derivatives. After protection of the isopropenyl group, the corresponding conversion into **2** was achieved by oxidative cleavage of the vinyl group by ozonolysis followed by the Jones oxidation (Scheme 3).

**Scheme 3.** Synthesis of kainic acid using the Ireland-Claisen rearrangement and palladium catalyzed formation of pyrrolidine ring.

The advantage of the recent synthesis of kainic acid, developed by Japanese scientists [23], unlike most other multi-stage syntheses, consisted in only nine stages. This short total synthesis was carried out on the basis of the Cu-catalyzed Michael addition-cyclization reaction between the bearing chiral auxiliary of camphorsultam-type isonitrile **27** and ester of unsaturated ketoacid **28** (in mixture with an isomeric ester) with Cu(t-ButSal)2 as catalyst (Scheme 4). The additional ester was isomerized into **28** in the conditions of this reaction. The treatment of the obtained adduct **29** by sodium methoxide with the loss of chiral substituent, followed by protection with (Boc)2O in the presence of triethylamine and DMAP, gave unsaturated pyrrolidine **30**. The alkaline hydrolysis of the amide group at the position 2 and the selective reduction of a double bond by boron-organic reagent l-selectride led to the protected analog of kainic acid **31** which was converted into the target compound via intermediates **32**–**34**. This synthesis was carried out with a 16.8% overall yield on chiral isonitrile and was conducted on a 300 mg scale, although usually syntheses of **2** had given smaller amounts of this product.

**Scheme 4.** Short total synthesis of (–)-kainic acid.

A gram-scale synthesis of (–)-kainic acid through six steps and with a 34% overall yield was elaborated using the Pt-catalyzed direct allylic amination [24]. This amination was catalyzed by the combination of dichloro(1,5-cyclooctadiene) platinum (Pt(cod)Cl2) with bis[2-di-phenylphosphino) ether (PPEphos). Such new approach allows catalyzing the direct introduction of amino group into allylic alcohols. The chosen scheme provided the minimum number of stages in this synthesis (Scheme 5). Amination of **35** under microwave heating conditions gave monoallylamine **36**. As a protective group

(Pg), authors preferred to use 2,4-dimethoxybenzyl (DMB) or p-methoxybenzyl (PMB) with almost no loss of enantiopurity. The unsaturated ester **38** was obtained by one-pot oxidation of epoxide **37** with 2,2,6,6-tetramethyl-1-piperidinyloxy (TEMPO) followed by the Wittig reaction. It was found that the transformation of **36** and **38** into diallylamine derivative **39** at a higher concentration inhibited the epimerization, and the desired product was obtained in 95% yield and 98% ee (enantiomeric excess). The following stage of heating in a sealed tube with xylenes and a catalytic amount of *i*Pr2N-Et gave pyrrolidine **40** in 75% yield and 14:1 diastereoselectivity. The Jones oxidation with application of H5IO6 as terminal oxidant gave satisfactory results at conversion of **40** into **2** (Scheme 5). Finally, 1.11 g pure kainic acid was obtained by this method in 34% overall yield. Taking into account its yield and the minimal number of steps, this proved to be the best chemical scheme elaborated to synthesize **2**.

**Scheme 5.** Synthesis of kainic acid using the Pd-catalyzed direct allylic amination.

Kainic and allo-kainic acids (**2** and **3**, respectively) were also synthesized using SmI2, a unique single-electron reducing agent that allows the induction of reductive coupling with the formation of C–C bond under mild conditions [25]. The key initial products for this new synthesis were obtained from D-serine methyl ester hydrochloride using the known procedure and a mixture of E- and Z-isomers of α,β-unsaturated esters, designated as E-14 and Z-14, with a general formula of **41**. The cyclization of **41** gave derivatives of kainic and/or allo-kainic acids (**42** and **43**) with different yields depending on ratio of the reagents (SmI2, hexamethylphosphoramide (HMPA), NiI2 and H2O) and additional ligands such as ethylenediamine, 2,2- -bipyridine, 2,2- -bipyridylamine, triphenylphosphine, etc. Pure compounds were isolated in optimal conditions of this reaction for each of them followed by separation and purification of the products through HPLC. The obtained compounds were converted into kainic acid **2** or allo-kainic acid **3** through a 3-step transformation by conversion ester groups into acids and deprotection of nitrogen in pyrrolidine ring (Scheme 6).

**Scheme 6.** Syntheses of kainic and *allo*-kainic acids using SmI2-induced cyclization.

The formal total synthesis of (–)-kainic acid, recently described by Chinese chemists [26], was based on a new approach to synthesis of the same important intermediate, which was already used for synthesizing kainic acid by different authors [19,20].

In general, there have been several syntheses of kainic acid published in recent years, which confirms the constant attention to this excitatory amino acid and the rapid progress of organic synthesis. These syntheses are shorter and can provide better production of kainic acid as a pharmacological probe for application in experimental pharmacology, although the costs and availability of the synthetic precursors and reagents used were not compared with previously known syntheses. All the studies have discovered a large number of kainoids, a total of 70 [26], including those obtained through recent chemical syntheses.


**4. Discussion**

Due to the limited time most people spend outside, the amount of ambient concentration of PM2.5 that people are directly exposed to is likely to be di fferent based on variation in people's behavior and the performance characteristics of the buildings they are occupying [55]. Consequently, spatial variability, time-activity and losses due to outdoor-to-indoor transport are all sources of exposure uncertainty in the epidemiological analysis, when fixed-site monitor concentrations are used as surrogates for exposure to air pollution. In this work we established a more comprehensive understanding of population exposure concentration and the impact that di fferent exposure metrics can make on all-cause mortality predictions. We showed that the I/O ratios and individual's patterns of movement play a key role in estimating exposure to PM2.5 and that transportation-MEs, predominately the highly polluted London Underground, are important in accurately establishing exposure. We demonstrated that subway and Indoor MEs make a significant contribution to the exposure misclassification and therefore mortality change predictions. Azimi and Stephens [31] highlighted the importance of including indoor MEs when estimating the total exposure and the need for a better understanding of how the infiltration factors vary by building type in order to improve the exposure estimates and reduce the uncertainty. Based on field measurements, they found that exposure to PM2.5 of outdoor origin inside the residence contributed around 67% to the total U.S. mortality burden. In our analysis, we found that the Indoor environment contributed approximately 83% to the total mortality burden in London. The di fference in our results may be explained by the di fferent MEs considered in each study. As our aim was to quantify the misclassification and give an insight into how the absence of significant MEs from an exposure assessment could increase the uncertainty, we mainly focused on the di fferent infiltration factors of home types and the LU. Martins et al. [56] determined the PM2.5 exposure and estimated the daily PM2.5 dose during Barcelona subway commuting. They estimated that the PM2.5 dose received by an adult in the subway contributed approximately 46% to the total daily dose in the respiratory tract. In our study, LU contributed approximately 15% to the total health burden. Due to the di fferent methods used and di fferent health endpoints, their results cannot directly be compared to ours. However, their outcomes indicate the non-trivial contribution from subway ME on health e ffects estimates. Several studies have compared static (home address-based) with more dynamic air pollution methods and proved that there is a reduction in average total exposure levels in urban areas with related characteristics as GLA [32,57]. Tang et al. [57] used a staged modelling approach to evaluate the use of static ambient concentrations as exposure estimates and examined the impact of dynamic components on estimated air pollution exposure. They found that the mean population exposures in Hong Kong for their full dynamic model were approximately 20% lower than the ambient baseline estimates of the static approach. Smith et al. [32] combined a dispersion modelling approach with building infiltration factors and travel behavior in order to create the London Hybrid Exposure Model (LHEM). They found that their model's estimates were around 37% lower for PM2.5 than the static approach (residential address-based). Similarly, by adopting a staged modelling approach to evaluate the e ffect of including dynamic components to our exposure models we found that the absence of mobility and infiltration factors in the static Tier-model 1 led to an overestimation of annual PM2.5 population exposure. Overall, the exposure estimates of our most complex model (Tier 5) were around 34% lower than those of the static baseline model (Tier 1). These findings were di fferent from Tang et al.'s [57] study but very similar to the LHEM study, mainly because the study population was the same and similar travel behavior data was used. Recently, Singh et al. [58] quantified the population exposure to PM2.5 concentrations in London and assessed the importance of including movement and indoor infiltration to total population exposure. They found that their refined exposure assessment predicted 28% lower total population exposure than the traditional static exposure method. As in this study, the time-activity data were derived from the LTDS [39] and the study area was London. However, the small di fference between their results and ours could be explained by the di fferent datasets used for the infiltration factors and the di fferent concept used for the key MEs (e.g., the London Underground). Results from other similar studies are di fficult to find as we compare di fferent exposure estimates during the same time period (2017) in an e ffort to examine the e ffect on all-cause mortality predictions. We showed that using a static exposure metric instead of a more dynamic approach (based on time-activity data and indoor infiltration) to predict the mortality in the GLA population would lead to an overestimation of 1174–1541 mean predicted estimates of mortality attributed to PM2.5. Ebelt et al. [59] found for several health outcomes associated with cardiopulmonary diseases, analyses with ambient exposures resulted in larger e ffect estimates. These results strongly supported their original hypothesis that the reduced exposure misclassification resulting from the utilization of ambient exposures instead of ambient concentrations provide more precise estimates of e ffects in epidemiology.

This work provides further understanding as to the impact of an exposure assessment on the mortality predictions and helps to mitigate the uncertainty in health risk assessments of air pollution. As a result, it would be possible to increase the e fficiency of regional or local air quality management strategies.

#### *Limitations and Future Work*

The current study contains several limitations. Only some of the deep and subsurface underground lines were monitored and only for a small sampling period. In this study, we assumed that these measurements also represented the corresponding lines that were not measured. Moreover, only 23 monitoring stations were available for PM2.5 and their locations were not uniformly spread across the study area. Consequently, this may have a ffected the simulation accuracy and the interpolated ambient concentration estimates in the unmonitored areas that were far from the stations that might have contained higher uncertainty. Furthermore, another limitation was the assumption that the Indoor microenvironment and the average dwelling I/O ratios also represented the o ffice and commercial buildings. The toxicity of PM2.5 was not included, but mainly because it was out of the scope of the study to investigate the toxicity of the particles.

The space–time–activity data is based upon the London Travel Demand Survey for the period 2005–2010 and may not be fully accurate locally, spatially and temporally, for the year 2017. Moreover, the annual average of the time-activity data that we used, assuming that people followed the typical daily mobility patterns for the whole year, may have increased the uncertainty in our models, because those data might not have accurately represented a part of the population. Since the main body of our study was based on averages and the population was not divided into di fferent age groups, our health burden predictions may be less accurate for special groups of people that have di fferent behaviours (e.g., ill or elderly that spend most of the day inside their residence).

Parameters that could a ffect particle infiltration, such as di fferences in indoor-outdoor air pressure due to the impact of the surrounding micro-environment, and the existence and e fficiency of mechanical filtration, were not the focus of the current study and were therefore not investigated.

In the future, this study could be improved by conducting further measurements in the London Underground and for larger periods of time. Simple sensitivity tests could be made in order to check each model's response and how the misclassification a ffects our estimates. As the next stage of this work we could investigate how this framework applies to other cities with higher ambient PM2.5 concentrations and di fferent indoor characteristics (such as interventions-PACs, HVAC). Taking into consideration that each urban area may have di fferent characteristics, it is important to examine how the incorporation of the local urban or building features could make an impact on exposure concentration estimates and health burden predictions.


**5. Periodic tasks**

The task concept is an abstraction of a program text. There are roughly three approaches to periodic tasks, depending on the primitives the operating system provides. Figure 5 illustrates the possible implementations of periodic tasks, where function *fi*() represents the body of task *τi* (i.e. the actual work done during each job of task *τi*).

Fig. 5. Possible implementations of a periodic task.

In Figure 5.a, the periodic behavior is programmed explicitly while in Figure 5.b this periodicity is implicit. The first syntax is typical for a system without support for periodicity, like *μ*C/OS-II. It provides two methods for managing time: *GetTime*() which returns the current time, and *DelayFor*(*t*) which delays the execution of the current task for *t* time units relative to the time when the method was called. As an important downside, the approach in Figure 5.a may give rise to jitter, when the task is preempted between *now* := *GetTime*() and *DelayFor*().

In order to go from Figure 5.a to 5.c we extract the periodic timer management from the task in two functions: a registration of the task as periodic and a synchronization with the timer system. A straightforward implementation of *TaskWaitPeriod()* is a suspension on a semaphore. Note that we wait at the beginning of the while loop body (rather than at the end) in case *φi* > 0. Going from interface in Figure 5.b to 5.c is now a simple implementation issue.

Note that the task structure described in Figure 5.b guarantees that a job will not start before the previous job has completed, and therefore makes sure that two jobs of the same task will not overlap if the first job's response time exceeds the task's period.

### **RELTEQ primitives for periodic tasks**

In order to provide the periodic task interface in 5.b, we need to implement a timer which expires periodically and triggers the task waiting inside the *TaskWaitPeriod()* call.

To support periodic tasks we introduce a new kind of RELTEQ events: a *period event*. Each period event *ei* points to a task *τi*. The expiration of a period event *ei* indicates the arrival of a periodic task *τi* upon which (i) the event time of the *ei* is set to *Ti* and reinserted into the system queue using *InsertEvent()*, and (ii) the semaphore blocking *τi* is raised.

To support periodic tasks we have equipped each task with three additional variables: *TaskPeriod*, expressed in the number of ticks, *TaskPeriodSemaphore*, pointing to the semaphore guarding the release of the task, and *TaskPeriodEvent*, pointing to a RELTEQ period event. For efficiency reasons we have added these directly to the Task Control Block (TCB), which is the *μ*C/OS-II structure storing the state information about a task. Our extensions could, however, reside in a separate structure pointing back to the original TCB.

A task *τi* is made periodic by calling *TaskMakePeriodic(τi, φi, Ti)*, which