**4.5 Acute lung injury**

Impaired pulmonary function is a well-known complication of cardiac surgeries, however it has multiple factors to be related with such as anesthesia, temporary cardiac dysfunction, infused catecolamines, altered mechanics of thoracic cage, duration of mechanical ventilation, neurological, renal and infectious complications rather than a single factor; CPB being the mostly accused. Although it is not possible to perform all the cardiac procedures without CPB, avoiding bypass alone cannot prevent the lung injury completely (Apostolakis et al,2010).

Although there are limited, insufficient data supporting the broad clinical use heparincoated circuits and miniaturized circuits, minimizing the extracorporeal surface area and being biocompatible and free of any material that activates blood should lower the incidence of lung injury (de Vroege et al,2004). Leucocyte depletion may reduce the entrapment into lung capillaries, that in experimental studies it has been shown to reduce the heart and lung reperfusion injury (Apostolakis et al,2010,as cited in Bando et al,1990). Ultrafiltration and controlled hemodilution reduce interstitial lung edema, improving the lung functions after surgery (Apostolakis et al,2010). Using a controlled cardiac suction device, reducing the time between the contact of shed blood with pericardium and its re-transfusion, becoming activated only when the blood is accumulated in pericardium-minimizing air entrance may also improve lung functions. Furthermore, since the heparin level of pericardial blood is lower than systemic level, topical heparin administration may also diminish the inflammatory reactions contributing to lung injury (Tabuchi et al,1993).

Apnoea during CPB has been shown to be associated with increased incidence of pulmonary dysfunction. The results of clinical trials are conflicting, that some revealed improvement in lung functions by maintaining ventilation (with or without CPAP) together with pulmonary artery perfusion during CPB, whereas others revealed no difference (Stanley et al,1977;John et al,2008).

The rules of myocardial protection during ischemia and reperfusion, indirectly protect the lungs from several proinflammatory factors produced during the process (Apostolakis et al,2010).


*Article* **An Ant Colony Algorithm for Improving Energy E**ffi**ciency of Road Vehicles**

#### **Alberto V. Donati 1,\*, Jette Krause 1, Christian Thiel 1 , Ben White 2 and Nikolas Hill 3**

Received: 14 May 2020; Accepted: 30 May 2020; Published: 3 June 2020

**Abstract:** The number and interdependency of vehicle CO2 reduction technologies, which can be employed to reduce greenhouse emissions for regulatory compliance in the European Union and other countries, has increasingly grown in the recent years. This paper proposes a method to optimally combine these technologies on cars or other road vehicles to improve their energy efficiency. The methodological difficulty is in the fact that these technologies have incompatibilities between them. Moreover, two conflicting objective functions are considered and have to be optimized to obtain Pareto optimal solutions: the CO2 reduction versus costs. For this NP-complete combinatorial problem, a method based on a metaheuristic with Ant Colony Optimization (ACO) combined with a Local Search (LS) algorithm is proposed and generalized as the Technology Packaging Problem (TPP). It consists in finding, from a given set of technologies (each with a specific cost and CO2 reduction potential), among all their possible combinations, the Pareto front composed by those configurations having the minimal total costs and maximum total CO2 reduction. We compare the performance of the proposed method with a Genetic Algorithm (GA) showing the improvements achieved. Thanks to the increased computational efficiency, this technique has been deployed to solve thousands of optimization instances generated by the availability of these technologies by year, type of powertrain, segment, drive cycle, cost type and scenario (i.e., more or less optimistic technology cost for projected data) and inclusion of off-cycle technologies. The total combinations of all these parameters give rise to thousands of distinct instances to be solved and optimized. Computational tests are also presented to show the effectiveness of this new approach. The outputs have been used as basis to assess the costs of complying with different levels of new vehicle CO2 standards, from the perspective of different manufacturer types as well as vehicle users in Europe.

**Keywords:** CO2 reduction; multi-objective combinatorial optimization; meta-heuristics; ant colony optimization

#### **1. Introduction**

In recent years, the automotive industry has faced increasing pressure to reduce CO2 emissions to meet regulatory targets set in both the EU and other legislations. Several new CO2 reduction and energy efficiency improvement technologies have been developed to respond to these new regulatory developments. This technology proliferation increases the complexity of finding optimal combinations to achieve substantial CO2 reductions in a cost-efficient way. The problem consists of finding feasible configurations, represented by points in the plane total CO2 reduction versus price, and identifying the limiting subset of those, which form the Pareto front, to yield the optimal configurations for the objectives defined. The optimization problem is computationally difficult because of the size of the search space. Because of incompatibilities between certain technologies, the optimization problem cannot be solved by simple sorting according to technology efficiency (CO2 reduction per cost) and constructing configurations starting by the most efficient technology, adding technologies based on their efficiency, from the most efficient to the least.

In the following, we discuss similar problems found in the literature and various approaches to tackle them. The Knapsack Problem (KP) and its multi-dimensional variant (MKP) are problems aimed to find an optimal subset of objects to be placed in one or more knapsacks (or limited capacity boxes/containers), maximizing the total profit or utility. These problems belong to the class of NP-complete problems and have been extensively explored as a fundamental area of discrete optimization and Operations Research (OR). Whilst the standard KP seeks to maximize the utility under a single constraint, other variations or generalizations can deal with the assignment of resources, such as in freight logistics planning, capital budgeting, allocation of tasks on processors, stock cutting problems or in distributed computing as presented in [1,2]. A well-studied extension of the MKP is the Multi-Objective Multi-dimensional Knapsack Problem (MOMKP), when taken with two objective functions and no constraints on the number or capacities of the knapsacks. A detailed survey of techniques to solve the Multi-Objective Knapsack Problem (MOKP) and MOMKP is presented in [3]; it discusses exact methods, approximation algorithms and heuristics for the MOKP, including Simulated Annealing, Tabu Search and other heuristic methods, subdivided into evolutionary algorithms and those based on local search (LS). Other methods, such as Fuzzy Logic and Kalman Filtering have been successfully applied in detecting failures of integrated sensors/systems on vehicles where high-dimensional data is present [4]. State-of-the-art genetic algorithms have been extensively applied to the multi-objective optimization like in [5,6], to the multi-criterion optimal design [7].

The Technology Packaging Problem (TPP) is introduced here since the problem differs considerably from a MOMKP, in that, if a knapsack is assimilated to a package or configuration:

The difference described in point (3) can be particularly noticeable if one would try to approach the problem by fixing one objective and trying to optimize the other. More specifically, this could be done by partitioning the cost (the knapsack capacity) from a minimum to a maximum cost, say in 100 slices, and solve the same number of optimization problems each with the maximum cost of the slice considered. However this would result in a loss of solutions, since the optimization would end with a pareto front with exactly the same number of solutions as slices and, further, a potential loss of efficiency in having to re-compute from scratch the solution for each slice. This would exclude for example the possibility of incrementally adding objects/technologies to already found optimal configurations. The reduction of the 2-objective problem to a sliced single objective problem has some drawbacks which will be discussed in Section 4. On the other hand, we have attempted the reduction of the current problem to other similar multi objective and constrained problems, but none brought to a satisfactory formulation without a significant loss of details or changes in the original problem. Hence, the introduction of the Technology Packaging Problem (TPP). To solve this problem with two (or more) conflicting objectives, we have devised an Ant Colony Optimization (ACO) combined with a Local Search heuristic (ACO + LS).

Bio-inspired algorithms have gained consensus as being some of the most efficient and effective to deal with complex problems and constraints in various areas of Operational Research. Examples include evolutionary algorithms, swarm intelligence, distributed systems, neural networks and similar heuristic/meta-heuristics methods for NP-complete or other computationally hard problems. In particular, the choice of ACO is motivated by the recent success of swarm intelligence approaches to solve complex and dynamical problems with a system of interacting agents, which combines exploration and learning (ACO) with simple local rules (LS). These approaches give rise to a collective and distributed intelligence through a form of system memory whereby global information is encoded locally, as presented extensively in [8,9]. ACO heuristics have proven to be particularly effective when combined with appropriate local search techniques, as evidenced in [10] and [11]. They are commonly used in operational research problems such as the classic Traveling Salesman Problem (TSP), quadratic assignment and job-shop scheduling. ACO extends to applications in problems of a dynamic nature due to the algorithm's adaptability and re-scheduling capabilities [12], for example the Vehicle Routing Problem (VRP) with variable traffic conditions [13] or with unexpected events [14]. In [15] ACO is used for the Multi-Stage Flow Shop Scheduling Problem and applied for the scheduling of real factories. The introduction of multiple colonies and different pheromones update strategies has been proved effective for multi-objective problems. An example is the application to the time and space assembly line balancing problem, presented in [16], where eight different ACO architectures are introduced and compared.

One of the first applications of ant colony inspired algorithms to the MKP and the Subset Problem (SP)—a special case of the knapsack problem—was made by [17] with an Ant System, a variation of ACO incorporating two distinct pheromone updating rules: local and global. This was followed specifically for the MKP by [18,19] (where a general ACO is introduced for multi-objective optimization) and later by [20], reporting very successful results in comparison to other evolutionary algorithms. The MKP, SP and TPP are quite different from TSP-like problems, as the ordering of objects or tasks to complete is not important: for the former the solutions are combinations, yet for the latter they are permutations (ordered sequences).

Some further considerations are therefore needed in the representation of the problem and in particular regarding the pheromone encoding. Two main approaches are present in the literature: node-based, deposited on the node or edge-based, deposited on the edges connecting nodes/objects. In this study we adopted the former, as presented in [18].

The TPP is formulated in Section 2, and the ACO + LS algorithm is discussed in Section 3. A short description of the construction of so-called cost curves for vehicle CO2 emission reduction is also given. In Section 4, the ACO + LS results are compared with those obtained previously with a Genetic Algorithm (GA). The real world optimization problem instances, discussed in Section 5, consist of a set of specific technologies which are available and a set of initialization parameters; these include the type of vehicle, its size and powertrain, the year considered and the drive cycle over which the technology is evaluated, which together act to determine the presence or absence of a technology. Finally conclusions are drawn in Section 6.

The contribution of this study is in the following:

be used for each manufacturer type's fleet composition and the combined CO2 emissions of its circulating vehicles.

With the above, once ran over the entire EU vehicle fleet typology has been used as an input for future CO2 targets, so one of its most relevant contributions has been to the support of the EU policy making to check the feasibility of the 2025–2030 CO2 reduction targets. The cases presented in Section 5 are just a part of a number of simulations/optimizations ran by the authors. Most of these, and their rationale, have been omitted since they are beyond the scope of this study. Thanks to its efficiency, the system has been used extensively for scenario analysis, for example to find configurations with certain technologies which are always present (such as where these technologies have already been embedded in the vehicles) or for the analysis in which different future scenarios for the price projection or CO2 reduction potentials were to be used. Overall, the authors have run about 60,000 problem instances in the course of these analyses.

#### **2. The Optimization Problem**

The TPP with two-objectives is defined as follows. Given:

The problem is then to find, among all the possible packages/configurations, those which are Pareto optimal with respect to two objective functions chosen. Since the technologies considered here are CO*2* reduction technologies, each with a cost *ci* and CO*2* reduction potential *ri* (which represents the CO2 emission reduction percentage value that can be attained with the technology in place), the objective functions *F* and *G* can be defined as the total cost of the package, *Ck* and its total CO*2* reduction *Rk* as follow:

$$C\_k = \sum\_{t\_i \in T\_k} c\_i \tag{1}$$

$$R\_k = 1 - \prod\_{t\_i \in T\_k} (1 - r\_i) \tag{2}$$

Equation (2) is obtained by considering the cumulative, interacting benefits of the technologies. For example, if technology 1 has *r*1 = 0.1 (i.e., 10%) and technology 2 has *r*2 = 0.05 (i.e., 5%), the total reduction of the combined technologies is obtained by applying technology 2 only after we have applied technology 1. Since technology 1 will provide a CO2 emission of (1−*r*1), applying the second will result in a total CO2 emission of (1−*r*1) × (1−*r*2). The total reduction of applying the two technologies is therefore *R* = 1− (1−*r*1) × (1−*r*2) (hence the form of Equation (2)), which given *r*1 and *r*2 is 0.145 or 14.5%.

For this specific application, the optimization is aimed at the minimization of Equation (1) and the maximization of Equation (2). Then the Pareto optimality condition between any two configurations *Tn* and *Tm* (independently of their size) is that: the configuration *Tn* is said to be dominating the configuration *Tm* if one of the two following conditions holds:

if:

$$\mathbb{C}\_{\text{ll}} < \mathbb{C}\_{\text{ll}} \text{ and } R\_{\text{ll}} \ge R\_{\text{ll}} \text{ or if } \mathbb{C}\_{\text{ll}} = \mathbb{C}\_{\text{ll}} \text{ and } R\_{\text{ll}} > R\_{\text{ll}} \tag{3}$$

This means that either *Tn* has lower total cost and same or higher total CO2 reduction, or it has the same cost but higher total CO2 reduction. The configuration *Tn* is said to be dominated by *Tm* if one of the following two conditions holds:

if:

$$R\_{\text{ll}} > C\_{\text{ll}} \text{ and } R\_{\text{ll}} \le R\_{\text{ll}} \text{ or if } C\_{\text{ll}} = C\_{\text{ll}} \text{ and } R\_{\text{ll}} < R\_{\text{ll}} \tag{4}$$

If a configuration dominates another one, this cannot belong to the set of the Pareto optimal front. On the other hand, *Tn* is Pareto optimal with *Tm* if one of the two following conditions hold:

if:

*Cn* > *Cm* and *Rn* > *Rm* or if *Cn* < *Cm* and *Rn* < *Rm* (5)

Geometrically, these conditions are equivalent to consider in the (*R,C*) plane, the relative position of the two configurations: *Tm* = (*Rm, Cm*) in which to center Cartesian axes, and *Tn* = (*Rn, Cn*). If the latter falls in the lower-right quadrant, including the vertical and horizontal semi-axes, then *Tn* is dominating (condition (3)) *Tm*, if it falls in the upper-left quadrant, including semi-axes, it is dominated (condition (4)), while in all other cases they are pareto optimal (condition (5)), so unless other configurations are found to dominate one or both, they are both included in the Pareto optimal set. The set of all Pareto optimal configurations is also referred to as Pareto front.

The size of a configuration is the number of technologies it contains. An exhaustive exploration of solution space, in case of *N* distinct technologies, would imply the evaluation of (1) and (2) for all possible configurations. The number of all possible configurations of size *n*, is the combinations of *N* objects taken *n* at a time: *N n* = *N*! *n*! (*N*−*n*)! . The total search space size, or the total number of all possible configurations (without considering incompatibilities), is then given by sum of the above expression over all possible configurations' sizes:

$$\mathbf{S} = \sum\_{n=1}^{N} \binom{N}{n} = 2^N - 1 \tag{6}$$

where the second equality derives from the Binomial theorem. Even considering incompatibilities (*I*) between technologies, which tend to diminish the number of the feasible combinations, from a rough estimation of the search space with (6), is where *N* is usually between 50 and 80 and *I* is in the order of 2 or 3, is it clear that an exhaustive search is computationally impracticable.

#### **3. The ACO Algorithm**

A simple brute force approach would imply testing all possible combinations of the compatible technologies, but this is computationally infeasible, given Equation (6). Some simple heuristics were tested. Technologies were sorted by their cost efficiency (also referred to as 'technology efficiency'), defined as the CO2 reduction divided by the cost, and to add technologies to the configuration one by one as far as they were compatible with those already present. In the construction process, when an incompatible technology is encountered, it is simply skipped, and the next is examined. This procedure is then repeated, starting each time with a different technology and adding technologies until no further technology could be added.

It was soon noticed that a procedure such as this omits some configurations, since it always starts from a pre-ordering of technologies, based on cost efficiency. Including some randomness greatly improved the number of configurations found. However, a purely random search coupled with a greedy ordering showed to use significant computational power without an efficient search strategy in

the solution space. It was observed that most of the time was spent randomly exploring, rather than reinforcing good solutions and discarding certain areas.

The Ant Colony Optimization (ACO) algorithm was therefore devised. This involves the creation of an underlying graph where artificial ants can propagate and lay pheromones, a mechanism which allows the local encoding of global information, as discussed in [8–11]. The representation of the problem is the following: each node of the graph represents a technology, and two nodes are connected by an edge if, and only if, they are compatible. If an edge is present, the ant can step from a node to the next and this walk translates into a building process, where at each step, a technology is added to the configuration completed so far. Before adding a new technology, the ant must check if it is compatible with all the technologies "visited" beforehand. To this aim, the ant needs to keep an updated incompatibility list, which contains all technologies incompatible with those previously visited. To each ant step corresponds the addition of a technology to form a configuration with one more object. As the new configuration is found, it is added to the ant's set of configurations found so far, which is then composed by a collection of configurations of increasing size. The ant's walk ends when there are no more nodes to visit, that is, when no further technology can be added to the last configuration found. At this point, all the configurations found are evaluated against those stored in the best solution found so far. If they are suboptimal, some Local Search (LS) might be attempted (generally, if the configuration size is between 6 and 12). Pheromones are updated by a uniform evaporation and a deposition, for each configuration, on the edges visited by the ant, proportionally to the quality of the configuration. The quality criterion of a configuration is its overall cost efficiency, i.e., the total CO2 reduction divided by the total cost. Otherwise, if the configuration is dominating or Pareto with respect to one or more stored in the best solution, pheromones are boosted (usually by a factor 1000) and proportionally to the quality of the configuration found. The configuration(s) of the best solution, dominated by the newly found configurations, if any, are identified and removed, and the better configuration (s) found, if any, are added to the best solution. In this way, the best solution keeps improving and stores the best configurations found so far by any ant.

#### *3.1. Technical Description*

All data pertaining to technologies and costs are fed into the system. The specific instance of the optimization problem is determined by choosing one value for each of the initialization parameters, i.e., year, powertrain, vehicle segment, drive cycle, cost scenario, cost type, and whether to include off-cycle technologies (see the list of parameter values in Section 5 for more detail). These values determine which technologies are present, their cost, their effective CO2 reduction, and their incompatibility list. With these elements, since each technology constitutes a node, the underlying graph can be constructed at this stage.

As noted before, we denote the cost efficiency of a technology its CO2 reduction per unit cost, briefly called technology efficiency (or node efficiency), as compared to the total CO2 reduction *R*, divided by the total cost *C*, called the 'configuration efficiency'. Certainly, using the configuration efficiency (*CE* = *R*/*C*) instead of a Pareto selection criteria on the two objectives would have resulted in optimized configurations as well, and namely on the *CE* itself. However, we wanted to explicitly require an improvement in each of the two objectives separately, as in criteria in Equation (5), since the aim was to have a set of configurations to choose from with separate criteria or objectives. For example, finding which configurations can reach a certain CO2 reduction level and the expected impact of the ambition of certain CO2 targets on actual costs.

The ACO algorithm proceeds as follows:

#### *3.2. Solution Construction*

(1) An ant *a* is created and placed in the first node *ti*, the first technology added to the configuration: the first node is chosen randomly in 10% of the cases and probabilistically otherwise, based on its efficiency. The ant's list of incompatible technologies is updated with the list of incompatible

technologies of this node. The node *ti* to-do-list is set "done" so it will not be added again and add the configuration to the solution the ant is constructing.

	- (a) Greedy (usually set to 20% of cases): go to node *tj* with the maximum *p* (*tj*) ∝ τ (*ti*, *tj*) × *e* (*tj*) where τ (*ti*, *tj*) is the pheromone on the edge (*ti*, *tj*), and *e* (*tj*) is the efficiency of *tj*; this is favoring exploitation and following the colony.
	- (b) Probabilistic (usually set to ~80% of cases): from node *ti* chose next node *tj* with a probability *p* (*tj*) as given above; this criteria provides a mix of exploitation and exploration.
	- (c) Random (usually set to 1% of cases): the ant goes to the next node *tj* randomly; this represents pure exploration/trial.

Then to determine *tj*, a second random number is drawn in the case of criteria b. or c., while if criterion a. is selected, *tj* is already determined, by argmax (*p* (*tj*)).

	- (a) Evaporation: all the pheromones on the graph undergo a uniform evaporation, i.e., all are decreased by a constant percentage (usually 20%) by multiplying by a constant ρ less than one; pheromones are never allowed to drop below a minimum level of 0.1 to avoid entering into stagnation,
	- (b) Deposition: first determine if a configuration is a new optimal or not (either dominating or pareto). If it is not, LS can be applied, as will be discussed shortly. Pheromones on the edges traversed by the ant are incremented proportionally to the configuration quality, as follows:
		- i. if not an optimal configuration: increase by ε = SF × *R*/*C* (*R* and *C* given by Equations (1) and (2)), and with a SF is a scale factor, depending on the problem size and costs involved.
		- ii. if a better or Pareto configuration is found, increase by ε = SF × B × *R*/*C*, where B is the boosting factor, in the order of 103.

Note that since each configuration is a combination of technologies (e.g., the order of the technologies does not matter), a deposition will be also be performed on all edges between all technologies contained. For example, if we consider the configuration {*t*3,*t*23,*t*54,*t*65} then the pheromones on edges (*t*3,*t*23), (*t*3,*t*54), (*t*3,*t*65), (*t*23,*t*54), (*t*23,*t*65), (*t*54,*t*65) and their reverse edges undergo i or ii above.

The schema of the ACO + LS algorithm is reported in pseudo code in Algorithm 1, which also calls the procedures of ant propagation, LS and pheromones update, reported in Algorithm from 2 to 4 below. The values of the ant parameters (e.g., the proportions of greedy, probabilistic and random steps, as well as the proportion of initial random step, ρ, *SF* and B) given above were chosen in the algorithm tuning phase, where several tests were conducted to obtain the highest quality solutions in the shortest time.

The ant algorithm, ant propagation and pheromone update mechanisms presented in this study are typical of those formulated in the ACO-related literature. It is worth noting that the pheromones are node based given that no ordering of the technologies in a configuration is needed. The ant choice criteria have been integrated here with the problem specific objectives, which drive the immediate reward. Also Local Search algorithm is bespoke, having been developed and integrated for this specific problem. The novelty is then how the ACO and LS have been applied to the specific problem.

Regarding the Local Search, three different methods of removing the technologies were attempted: removing randomly, removing the least efficient technologies and removing those with the largest

incompatibility list. After a few tests, substituting technologies only on the basis of efficiency appeared to be the best criteria. Once the technologies have been removed, the same number of technologies (compatible with the reduced configuration) are randomly chosen and inserted. The modified configuration is then compared to the original and, if it is better, exchanged with the previous one in the ant's solution.

**Algorithm 2**. Ant propagation

**Algorithm 3**. Local Search

```
1 • Pick the ns nodes with lowest cost efficiency and remove them from Tk:
2 for r=1,..., ns, Tr = Tk-{tr1, ... , tr} (typically nLS=1 or 2)
3 • Update the incompatibility list λr for the reduced configuration Tr
4 • repeat ns times:
5 -
         Among the compatible nodes with λr pick one randomly and insert it in Tr
6 -
         Update λr
```
**Algorithm 4**. Pheromones update

```
1 • Evaporation: ∀ i,j, with i-
                                  j: τij=max(ρ∗τij,0.1)
2 where ρ is the persistency constant (0<ρ<1, typically ρ=0.8)
3
4 • Deposition: for i,j such that ti and tj∈Tk: τij=τij + ε
5 where ε is the deposition level (ε>0)
```
The Local Search is typically run for intermediate configurations sizes, usually of 6 to 12 technologies, since this is a greatly populated area in the solution space. It is performed only if the configuration is not an optimal one. It proceeds by removing one or more technologies from some chosen configuration and re-calculating the incompatibility list without the removed technologies; finally, some alternative technologies, compatible with the reduced configuration, are added. The number of technologies to switch can be chosen by the user, but as it heavily affects the efficiency, it was typically set to 2.

Finally, we note that many tests have been conducted in order to identify reasonable values for the various algorithm parameters over a wide set of problem instances which guarantee convergence to high quality solutions in timeframes on the order of 10 min. Detailed discussion of these tests is beyond the scope of this paper.

#### **4. Results and Benchmarking**

The ACO + LS algorithm presented in the previous section, was tested for a wide range of optimization scenarios and employed to calculate optimal solutions, which are the inputs for the calculation of the vehicle CO*2* emission reduction cost curves, as introduced in [21]. For cars and vans this included optimizations for all powertrains and vehicle segments, with and without off-cycle technologies, for 2025 and 2030 under the Worldwide harmonized Light vehicles Test Procedure (WLTP) test cycle (i.e., 224 optimization runs), and a number of other cases, as presented in Section 5 and in [22,23]. Moreover, the tool was employed for a number of additional computations. Each optimization was run for 10 min, yielding high quality solutions for the given algorithm parameters. By performing these runs in parallel, it was possible to complete overnight (14 h run-time) even a larger set of runs, up to 2000, taking advantage of 32 processors-threads of the Intel® Xeon® CPU E5-2687W at 3.10 GHz.

In an earlier approach, a heuristic method was applied for assessing vehicle CO2 reduction technologies and costs as presented in [24]. In order to solve the problem, a cloud of data points (i.e., tuples of CO2 emission reduction and cost values) was created from all possible combinations of different CO2 reduction technologies. This method was able to generate results for problems up to about 30 technologies. However, it proved to be inefficient for larger and constrained problems, so to handle these, a more efficient heuristic was developed, at first utilising a procedure where the configurations are determined iteratively by using a set of constraints based on incompatible technologies, with a Genetic Algorithm (GA) to create and select optimized solutions.

Genetic Algorithms were proved to effectively solve a number of optimization problems and also in particular the MKP, as reported in [25]. In recent years, such algorithms have also been developed and made available in MATLAB® as a toolbox [26].

Since the current problem has two objectives, a possible approach with a GA is to divide the cost range into a certain number of 'slices' (usually a few hundred, from a minimum to maximum pre-determined cost range). In each cost slice, the algorithm seeks configurations with the maximum CO2 reduction value, taking into account the compatibility constraints. The problem is thus reduced to a single objective optimization in each cost slice. In a preliminary study, it appeared crucial to establish an appropriate number of these slices since it heavily influences the computational time. The authors reported, after running some tests on an Intel Core i5-3340M CPU at 2.70 GHz, that a value of 200 slices yielded a reasonable computation time of 4-5 min per instance, as compared to 100 slices (about 2 min), 400 slices (about 10 min) and 1000 slices (about 30 min). This choice was also made since further increasing the number of slices, was not adding a sufficient improvement to justify the increased computational times.

The GA approach demonstrated to be able to handle problems of up to about 50 technologies. The computational work was carried out in 2015 using MATLAB® R2013a, using the function *ga* which is contained within the MATLAB® Global Optimization Toolbox to launch the optimization [27]. This function, if not specified by the user, automatically adjusts the levels of mutation and cross-over for the constrained optimization problem, as the total cost of a configuration must lie in the cost slice. The slices are created by computing, for each case, the minimum and maximum cost. The former is the minimum cost among the technologies considered in the instance problem, while the latter is the sum of the cost of all the technologies. The schema of the algorithm is given in Algorithm 5.

Although this approach is quite efficient, as the number of CO2 reduction technologies start to increase, it becomes computationally more and more inefficient and time consuming. We recall that the search space grows very quickly, as 2*N*, where *N* is the number of technologies.

**Algorithm 5**. Genetic Algorithm

In the following section, ACO + LS outcomes have been compared against the outcomes achieved with the GA approach. The GA runs are the benchmark runs, i.e., they were not repeated for each problem instance, but rather each is the best solution found over all the runs made for that instance; for some instances different slice resolutions were ran, with different times to improve solution quality. We have reported and compared with the best solution found for a particular instance.

Figure 1 shows an example of the CO2 reduction/cost-tuples resulting from both approaches, run for a typical case. The coloured (or grey) dots represent ACO + LS solutions, where the colours (or grey shade) indicate the size of the configuration. The black squares represent the GA benchmark solutions.

**Figure 1.** Comparison of CO2 reduction and cost for optimal configurations found by GA and ACO + LS. Black squares represent GA configurations while colored (or grey) dots represent ACO + LS configurations. The color (or gray shade) represent the configuration size).

For this case, we note that there are a total of 76 Pareto optimal GA configurations (points), against the 287 optimal configurations found with ACO + LS. Moreover the GA configurations are almost always dominated by the configurations found by ACO + LS. The high number of solutions found by the ACO + LS makes the Pareto front more densely populated, which is desirable when searching differentiation or alternative configurations.

We note also a side effects of the GA slicing: the best solution found in a slice, can be dominated by solutions in other slices. In other words, dividing the 2nd objective by M slices, does not guarantee that the Pareto front will have M points. In this case, pareto-optimal configurations are not found in some slices (as their existence can be seen by the ACO + LS), therefore the GA algorithm converges to sub-optimal configurations in a number of cases.

The two noticeable discontinuities are due to two high-cost technologies, usually characterized also by a high CO2 reduction; these, once added to a lower size optimal configuration, result in sudden increase of the additional cost and CO2 reduction, thus appearing as a discontinuity of the pareto front.

For a more systematic comparison, ten problems were chosen, as listed in Table 1, with different initial parameters for year, powertrain, vehicle segment and test cycle, where GA benchmark solutions were available. For Problems 2, 8 and 9, the GA was run over 1000 iterations (1 h runs).

**Table 1.** Reference table of the benchmark problems, with typical cost scenario and total cost type. See Nomenclature for abbreviations or Section 5 for extended descriptions of the parameter values.

For each problem, ten repeated ACO + LS runs were made with a runtime of 10 min and 1 h, respectively. Then ACO + LS solutions (sets of all configurations found) were compared to GA benchmark solutions in the following way.

Accordingly, to the optimality criteria defined in Equation (3), a configuration of a solution is dominated if there is at least one configuration of the second solution that is dominating it. Therefore, to have an appropriate comparison of two Pareto optimal fronts, also called best solutions found or simply solutions, *S1* and *S2*, each configuration *Tk* of the solution *S1* has to be compared to the set of configurations of the solution *S2*. An evaluation is made to check if *Tk* is dominating, dominated, additional Pareto for *S2* or equal (exactly the same cost and CO2 reduction, with 2 precision digits for costs and 8 precision digits for CO2 reduction - these values were set considering the precision of the GA benchmark configurations at hand). If it is dominating, the number of dominated configurations of *S2* is estimated.

Then the reverse is done, to evaluate, one by one, how many configurations of *S2* are dominating, dominated, additional Pareto, or equal with respect to *S1*. Since ACO + LS runs were repeated 10 times for each problem, for each run, the number of dominating, additional Pareto, equal and dominated configurations were computed for each run.  The runs of 10 min and 1 h are reported in Tables 2 and 3, respectively.

*Energies* **2020**, *13*, 2850

**Table 2.** Comparison of ACO + LS 10-min runs with GA Benchmark runs. Results of 10-minute ACO + LS runs, each repeated ten times for each problem, versus GA benchmark solutions. In the first part of the table, GA configurations are compared to ACO + LS, while in the second, vice-versa. The means are computed over the ten runs, and values in italics indicate the 95% confidence interval (CI) of the estimate of the mean. Boldface is used to indicate when GA has better or additional solutions than those found by ACO + LS.

*Energies* **2020**, *13*, 2850

The tables contain the comparison of the GA benchmark solutions *S*GA versus ACO + LS solutions *S*ACO+LS (first five columns), and then the reverse (last five columns). In particular, the size of the benchmark solutions is reported (in the tables, the column "configs. found", column 1), which is measure of the search efficiency of the respective algorithm: the greater the number of configurations, the greater is their diversity and thus the possibility of combing technologies/objects in different ways. It is also reported, for each problem, the mean number of configurations of *S*GA that are better than *S*ACO+LS, and the mean number of configurations of SACO+LS that are dominated by the *S*GA. Then, in the following columns 4 and 5, the mean number of additional Pareto configurations, and the mean number of equal configurations are reported (equal configurations is the only quantity which is invariant when comparing *S1* to *S2* or vice-versa). In the second part of the tables, columns 6–10, the reverse comparison is made, for the configurations of *S*ACO+LS versus *S*GA.

In the tables, it can be seen that the GA benchmark solutions contain additional optimal configurations in just a few cases to the optimal configurations found by ACO + LS (in the tables, these cases are marked in bold).

A value of 0.10 in column 2, means that, over 10 repeated runs, one GA configuration was better than some ACO + LS configuration (s); the value of 0.10 in column 3, tells that only one ACO + LS configuration was dominated by that GA configuration. In some other cases, a few additional Pareto configurations were found by GA. Finally a few other configurations are the same exact configurations as found by ACO + LS (the columns 5 and 10 are indeed the same). Most relevantly, in most cases ACO + LS configurations dominate the GA configurations (columns 7 and 8) or are additional Pareto (column 9) to those. The number of optimal configurations found by ACO+LS and GA is reported in columns 1 and 6 respectively, and the different order of magnitude of the two is noticeable. As computation time is increased from Table 2 to Table 3, the ACO + LS algorithm finds a slightly higher number of optimal configurations and convergence is increased over the ten repeated runs, as shown by the consistent decrease of the 95% confidence interval. Also the number of the ACO + LS dominated configurations shows a clear tendency to diminish further, as well as the GA additional Pareto solutions to ACO + LS.

It is possible to run the ACO + LS algorithm for many hours in an attempt to find even better solutions, or repeat the same runs to see how consistent is the convergence to the final solution. On an Intel Xenon at 3.10 GHz, it was found that approximately 10 min is suitable to find an optimized solution using ACO + LS. Given the high number of configurations found, a substantial improvement with respect to the GA is achieved. Depending on the number of available technologies of the problem instance, the ACO + LS algorithm typically completes between 30 and 60 million iterations (ants) in 10 min.

Besides the 10 problems discussed above, an indirect comparison was made between the fits found by the new approach for several thousand input datasets and cost scenarios. For each, the optimal configurations are fitted with a Levenberg-Marquardt non-linear regression algorithm using the *nlsLM* function of the minpack.lm library in R [28]. This provides a so-called cost function, the analytical form of the Pareto front, as documented and used in [22,23], which are then used for various other applications. This extensive exercise enhanced the authors' confidence in the efficiency and robustness of the ACO + LS algorithm.

Finally, as a further evaluation of the ACO + LS method, we examined the cloud of points in the CO2 reduction/cost plane pertaining to the configurations the ants visited during their search. By inspecting the cloud, the ant colony's distance to the Pareto front can be evaluated. If the colony stays close enough to the optimal solution, it has a higher likelihood of finding improved configurations and avoiding stagnation in local minima. The search process is driven by the parameters which determine the ants' behavior, particularly exploitation versus exploration settings (the relative share between greedy, probabilistic and random choices during propagation). The following Figure 2 shows this cloud for Problem 1. Each of the small points relates to a configuration found by the ants during their search. The points are a random extract of 5000 points over the last part of the run; only this small

number of points is extracted for visualization purposes, since the total number of points are several millions, for a typical run. Similar plots were also made for initial or intermediate intervals of the run, to monitor the activities of ants in the colony.

**Figure 2.** Randomly sampled intermediate configurations visited by the ants (small blue dots) along with optimal configurations found by ACO (colored or grey shades-bigger dots of the Pareto front).

The most significant ant parameters were varied and studied. We found that for runs of 10 min or greater, there were no significant differences in the colony behavior between the first and last fifth of iterations once the algorithm is properly tuned. This means that 10 min are sufficient for the ant colony to stabilize and indicates that it is likely that even less time would be required to find optimal solutions for the problems considered. In Figure 2 the optimal configurations (the Pareto front) found by ACO + LS are plotted indicating the number of technologies present in the particular configuration. Ideally, the cloud generated by ACO + LS should stay as close as possible to the optimal solution (which also changes during the optimization process) such that small variations in the ants' paths (explorations) can make them find new optimal configurations.

We note that configurations of intermediate size, i.e., 6 to 12 technologies, constitute the most populated area. Figure 2 visually shows why it was decided to run LS only on points in this region: to enhance exploitation in this highly populated area of the search space. Mathematically, this is due to the summation term in (6), where combinations are at their maximum when *n* = *N*/2.

In summary, the ACO + LS approach has been shown to be able to handle the computational complexity of the Technology Packaging Problem quite efficiently, in particular with the following benefits:

In particular, as a consequence of (3) above, the so-called technology pathways can be identified, i.e., certain points or regions of the Pareto front can be associated with different configurations that reach them in the most cost-efficient way from sub-optimal configurations, just by adding specific technologies. In other words, it is possible to gather concrete indications as to what are the most suitable technologies to add to pre-existing packages to achieve given CO2 reduction targets at a minimum cost, so that the transitions from these packages to new optimized ones can be done with ease and continuity.

We finally note that due to the novelty of the problem, we could not compare with other algorithms in the literature. This study aimed to present a method that could solve this problem most efficiently and with high quality solutions. This method could constitute a benchmark for future algorithms to solve this problem, and certainly provides clear indications that it is exploring extensively and efficiently the search space. The same methodology was later applied with minor changes to provide input for discussing CO2 reduction technologies for heavy duty vehicles, which demonstrates the flexibility of the method.

#### **5. Applications**

We applied the method to identify optimal technology packages for reducing CO2 emissions from light duty vehicles (LDVs, i.e., passenger cars and vans) [22] and later also for the heavy duty vehicles (HDVs) [23]. The approach presented in this study was employed within the analytic work supporting the impact assessments for post-2020 LDV and HDV CO2 standards in the EU.

A dataset of more than 80 LDV CO2 emission reduction technologies, including their reduction potentials, costs and mutual compatibilities was provided in [29]. Similarly, an extensive study was carried out for HDVs with data in [30]. For each problem, once the optimal configurations are found with the ACO + LS, a so-called cost curve can be found by fitting, and it describes the Pareto front of costs versus CO2 reductions in a continuous analytical form.

A number of values for the initialization parameters exist, which define a specific instance of the optimization problem. For the LDVs these are namely (a list of abbreviations is also available in Nomenclature Section):

	- Spark Ignition (i.e., gasoline) Internal Combustion Engine and Hybrid (SI ICE + HEV)
	- Compression Ignition (i.e., diesel) Internal Combustion Engine and Hybrid (CI ICE + HEV)
	- Spark Ignition Plug-in Hybrid (SI PHEV)
	- Compression Ignition Plug-in Hybrid (CI PHEV)
	- Spark Ignition Range-Extended Electric Vehicle (SI REEV)
	- Compression Ignition Range-Extended Electric Vehicle (CI REEV)
	- Battery Electric Vehicle (BEV)
	- Fuel Cell Electric Vehicle (FCEV)
	- Direct cost, i.e., manufacturing costs
	- Total costs, i.e., direct costs plus indirect costs, where indirect costs represent additional costs related to a technology such as R&D, warranties, marketing, etc.

The possible combinations of all these parameters give rise to a total combination of 6048 different instances. For the sake of most of applications, we set the cost type equal to total cost, which results in a total of 3024 possible optimization problem instances to be run for each scenario. This is quite a computational effort: if ran in sequence they correspond to about 21-day computation time for 10 min runs, or 7 days if only interested in WLTP cycle.

It is to be noted that the set of available CO2 reduction technologies are updated infrequently, depending on the availability of new data on technologies and their costs as well as on policy needs. Therefore, the addition of new technologies doesn't constitute any computational issue, as well as the variation of their costs, having been taken into consideration via 3 technology cost scenarios in the construction of the Pareto front for modelling the Cost Curves.

#### *Cost Curve Construction*

On the basis of the optimal configurations found by the ACO optimization described in the previous sections, vehicle emission reduction cost curves can be constructed to interpolate analytically these data points. The construction of cost curves consists in the fitting of the Pareto front configurations/points. This fitting procedure was a considerable part of the work, and it is worth noticing that all the Pareto fronts were fitted with an analytical form deriving by one single generalized functional form, with at most 4 fitting parameters. The cost curves are an essential input for applications such as the evaluation of different CO2 reduction scenarios, e.g., for calculating the costs associated with a certain desired CO2 standard for vehicles, identifying cost-minimizing distributions of CO2 reduction efforts across different vehicle types and segments.

A number of post-processing steps had to be applied to the raw output from the optimization procedure, to take into account some necessary adjustments. The post-processing steps, the cost curve fitting procedure and the resulting cost curves are documented in [22] for LDVs, and in [23] for HDVs.

We have conducted extensive computational campaigns to address a number of variations and hypotheses for scenario analysis, considering variations of the original problem instances where for example a certain technology is desired to be always present, or in another case, where a certain technology could be considered to be less effective (in term of CO2 reduction) than expected or some other more costly, to derive the optimal configurations and related cost curves. These results, obtained in relative short time scales, were made possible thanks to the properties of fast convergence and efficiency of the ACO + LS methodology.

#### **6. Conclusions**

We have presented a method based on a metaheuristic with Ant Colony Optimization combined with a Local Search algorithm to efficiently solve the Technology Packaging Problem, of finding feasible configurations of CO2 reduction technologies, maximizing the CO2 reduction and minimizing costs.

The results presented show that this methodology provides solutions of increased quality and size versus other approaches, e.g., genetic algorithms, thus offering the possibility of finding improved configurations in a highly efficient and easily adaptable manner. This has enhanced the capability to explore optimal strategies under increasingly numerous and complex technology choices against various CO2 reduction targets. The method can easily accommodate more technology options or perform analyses of different technology pathways. As the runtime is greatly improved, it has been employed to run multiple scenario analyses, including e.g., variations in technology impact, costs, and test cycles.

This method has been employed for supporting the impact assessments for post-2020 CO2 standards for cars, vans and trucks in the EU, considering diverse road vehicle fleet compositions. Different scenarios for the post-2020 EU CO2 emission standards have been evaluated by constructing vehicle emission reduction cost curves as described in the present paper, and, building on them, identifying an optimal distribution of efforts among different powertrains and segments which compose the fleet, calculating additional manufacturing costs as well as fuel savings, and computing total additional costs or savings from emission reduction. Further to applications in policy support, this methodology could also be applied by the automotive industry itself.

**Author Contributions:** Conceptualization: A.V.D., J.K. and C.T.; methodology: A.V.D., B.W. and N.H.; software: A.V.D., B.W. (for GA); validation: J.K. and C.T.; formal analysis: A.V.D.; investigation: N.H.; data curation: N.H. and B.W.; writing—original draft preparation: A.V.D.; writing—review and editing: all authors; visualization: A.V.D.; supervision: J.K. and C.T.; project administration: C.T.; funding acquisition: N.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported through the Horizon 2020 direct funding of the Joint Research Centre (JRC) of the European Commission, for the JRC authors. The work of the other authors was funded by the European Commission under the framework contract CLIMA.C.2/FRA/2012/0006. The views expressed are purely those of the authors and may not in any circumstances be regarded as stating an official position of the European Commission.

**Acknowledgments:** The authors wish to thank Yannis Drossinos (JRC), for the valuable comments and suggestions.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Nomenclature: List of Abbreviations**

#### **References**

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*


**6. Agronomic management practices to cope up with abiotic stresses**

In order to cope up with abiotic stresses, other than the plant's own defensive mechanisms, changes in management practices play a considerable role. Few such agronomic management practices have been listed in **Table 2**. Improved breeding

#### **Table 2.**

*Agronomic management practices to alleviate abiotic stresses.*

program to develop highly resistant varieties is also needed. In this regard, identification of responsive genes against abiotic stresses is one of the frontline strategies that can be made by the plant breeders. Use of genetic engineering to develop transgenic

#### *DOI: http://dx.doi.org/10.5772/intechopen.106045 Influence of Abiotic Stresses on Seed Production and Quality*

plants keeping in mind the safety of the environment and human health is the another key solution against abiotic stresses. It has been also observed that use of beneficial microbes in the form of seed bio priming has helped the plant to alleviate against such abiotic stresses through its positive role in germination and early plant stand establishment [66]. Bio priming increases the osmolyte concentrations which results in high cell wall elasticity and turgid weight to dry weight ratio. Further, endophytic synthesis of alkaloids can save the macromolecules from ROS through ROS scavenging activities. Plant growth promoting rhizobacteria (PGPR) enables expression of drought response related genes through enhancing ROS scavenging activities. Synthesis of phytohormones like IAA, GA3 etc. occurs due to PGPR which in turn, helps in plant growth under stress condition. PGPR further synthesizes exopolysaccharides which improves soil structure and maintains water and nutrient uptakes. Exogenous application of various chemicals viz. proline, glycine betaine, trehalose etc., plant components such as amino acid, sugars etc. and phytohormones such as ABA, GA3, jasmonic acid, salicylic acid, brassinosteroids etc. can help plant to cope up with abiotic stresses. Salicylic acid is a phenolic phytohormone which at low level can alleviate abiotic stresses through improving stomatal regulation, leaf chlorophyll content, water use efficiency and root growth. Brassinosteroids induce expression of antioxidant genes and thus, alleviate oxidative stresses through synthesizing ABA, proline, glutathione, phytochelatins, heat shock proteins and stimulating N metabolism. Brassinosteroids and salicylic acid help in improving seed production and quality under salinity stress through osmoregulation by increase of SOD, POD, CAT activities and elevation of photosynthesis. Jasmonic acid is a cyclopentanone derivative synthesized from linolenic acid, which helps the plants to tolerate abiotic stresses. Application of silicon also plays important role in improving crop growth and productivity under abiotic stresses. Under stress conditions, plant itself synthesizes proline, L-tryptophan, glutathione (GSH), citric acid, polyols, lipoic acid, ascorbic acid, glycine betaine, α-tocopherol, melatonin etc. as defensive mechanisms. Further, exogenous application of these can be found useful in alleviating stress. Proline acts as a ROS quencher by increasing the activities of SOD, CAT etc. and maintains plant growth under drought or salinity stress. L-tryptophan (amino acid) can synthesize auxin and thus, helps in plant growth. Glutathione (GSH) is a low molecular weight tripeptide, composed of glutamine, cysteine and glycine. Application of GSH detoxifies ROS, methylglyoxal and synthesize phytochelatins which bind heavy metal. It also acts as cysteine reservoir [67]. Citric acid is the intermediate product of TCA cycle, which is produced by citrate synthase from oxaloacetate and acetyl coA. Application of citric acid shows antioxidant properties which inactivates heavy metals such as Cu, Pb, Al etc. as well as protects the plant from salinity stress. Foliar application of polyols or sugar alcohols (mannitol, sorbitol, inositol) plays positive role in osmotic adjustment through improving SOD, POD, CAT activities and thus, helps the plant to cope up with drought, salinity and heavy metal stresses. Lipoic acid application on plant canopy under salt or drought stresses reduces lipid peroxidation and increases cysteine, POD and CAT activities. Ascorbic acid or vitamin C application can neutralize ROS under drought stress. Under low temperature, seedling subjected to incubation in ascorbic acid can reduce oxidative damage and improves proline, nutrients and CAT activity. Glycine betaine application under salinity or drought stress helps the plant in osmotic adjustment and stabilization of PS II. α-Tocopherol is generally present in chloroplast and improves photosynthetic membrane integrity. Exogenous application of α-tocopherol reduces oxidative damage by scavenging ROS under abiotic stresses. Melatonin is a tryptophan derivative which increases proline

synthesis and further, acts as ROS scavenger. Further, soil and foliar applications of humic substances, beneficial fungi, bacteria, chitosan, sea weed extracts etc. can play positive roles in combating abiotic stresses.


**5. Summarize**

*The Human Auditory System - Basic Features and Updates on Audiological Diagnosis and Therapy*

auditory information outside the therapy room. Some simple changes may bring many benefits to learning. Common recommendations for individuals with audi-

*test verbalizing; FPT H = frequency pattern test humming; SSI = synthetic sentence identification.*

*Comparison of performance in behavioral evaluation pre and post intervention by groups. ATG Pre = auditory training pre intervention; ATG Post = auditory training post intervention; VTG Pre = visual training pre intervention; VTG Post = visual training post intervention; DD = dichotic digits; FPT V = frequency pattern*

tory disorders include the following:

• Addition of visual cues

• Repetition or rephrasing

• Provision of a notetaker

• Positive reinforcement

• FM systems

• Recording information pictorially

• Gaining attention prior to speaking

• Reducing environmental noise

• Clear language

**Figure 7.**

• Preferred seating arrangements

• Making frequent checks for understanding

• Multimodality cues and hands-on demonstrations

• Preteaching of new information and new vocabulary

**76**


**5. Conclusions and the Way Forward**

The goal of telemedicine is the early identification and diagnosis of AECOPDs and timely access to appropriate treatment in order to improve patient outcomes. An important issue for the effective application of telemonitoring in COPD is the parameters monitored in terms of reliable prediction of an AECOPD (e.g., FEV1, symptoms, pulse oximetry). When an algorithm is used, the design is even more crucial regarding the satisfactory sensitivity and specificity of the method. Moreover, the effort that was required from the patients was not associated with the outcomes of the studies; however, it is likely that patients would be more compliant to interventions that would require minimal effort on their end. The results of the negative studies regarding exacerbations, hospital admissions, ED visits, and total GP calls could be attributed to the short telemonitoring period and the fact that in most of them, healthcare utilization and exacerbations were not the primary endpoints. Importantly, the small number of participants may have led to negative results and may have additionally precluded the extraction of safe conclusions. Additionally, the short follow-up periods of many studies were not long enough to capture the natural history of AECOPD, and in our opinion a minimum of 12 months would be required. Moreover, adherence to inhaled medication, which is enhanced during RCTs, may additionally lead to reduced AECOPDs and hospital admissions in both intervention and usual care groups, thereby resulting in non-significant differences. The commitment and appropriate training of the study team involved in such programs is crucial for successful telehealth services, while the interruption of monitoring during weekends by healthcare interventions may have affected the results in some cases, although prevention of exacerbations was reported without active intervention by the study team, which was attributed to better disease awareness and self-management. Finally, it is unknown which patients will benefit more from telemedicine, and larger RCTs are needed with subgroup analysis to define the most appropriate population for telemonitoring interventions. These studies would need to be of a proper length and size, and following a multifactorial evaluation would need to involve the appropriate participants who will adopt these telemonitoring interventions, which ideally will require minimal patient effort, in order to maximize the engagement and potential benefits. The proposed characteristics of future studies are summarized in Table 3.

**Table 3.** Proposed characteristics of future studies.

**Author Contributions:** Conceptualization, A.K., A.G., C.K., K.I.G. and K.K.; methodology, C.K., A.G. and A.K.; software, C.K., G.N., N.G. and A.G. validation, C.K.,A.G., A.K. and K.K.; formal analysis, C.K., A.G. and G.N.; investigation, C.K. and A.G.; resources, A.K. and K.K.; data curation, C.K., A.G., A.K. and K.K.; writing—original draft preparation, C.K., A.G., G.N., N.G. and K.I.G.; writing review and editing, A.K. and K.K.; visualization, A.G.; supervision, A.K.; project administration, K.K.; funding acquisition, A.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** The present study was partly funded by the project entitled 'PRECURSOR: PREventing COPD URgent States Of Relapse', co-financed by the European Union and Greek national funds through the Operational Program for Research and Innovation Smart Specialization Strategy (RIS3) of Ipeiros (Project Code: HΠ1AB-0028176).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data generated or analyzed during this study are included in this published article. Anonymized data will be shared uponrequest from any qualified investigator.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

### **References**

**Shih-Lung Cheng 1,2,\* and Ching-Hsiung Lin 3,4,5**

**Abstract:** Chronic obstructive pulmonary disease (COPD) is a preventable and treatable disease that is associated with significant morbidity and mortality, giving rise to an enormous social and economic burden. The Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Pulmonary Disease (GOLD) report is one of the most frequently used documents for managing COPD patients worldwide. A survey was conducted across country-level members of Asia-Pacific Society of Respiratory (APSR) for collecting an updated version of local COPD guidelines, which were implemented in each country. This is the first report to summarize the similarities and differences among the COPD guidelines across the Asia-Pacific region. The degree of airflow limitation, assessment of COPD severity, management, and pharmacologic therapy of stable COPD will be reviewed in this report.

**Keywords:** COPD guideline; Asia

**Citation:** Cheng, S.-L.; Lin, C.-H. COPD Guidelines in the Asia-Pacific Regions: Similarities and Differences. *Diagnostics* **2021**, *11*, 1153. https:// doi.org/10.3390/diagnostics11071153

Academic Editor: Koichi Nishimura

Received: 1 June 2021 Accepted: 21 June 2021 Published: 24 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Although chronic obstructive pulmonary disease (COPD) is a preventable and treatable disease, it is associated with significant morbidity and mortality, giving rise to an enormous social and economic burden. The results from the Epidemiology and Impact of COPD (EPIC) Asia population-based survey suggest a high prevalence of COPD in the participating Asia-Pacific territories [1] and indicate a substantial socioeconomic burden of the disease in this region. Individuals with the disease reported substantial limitations in their daily activities and loss in work productivity. To address this situation and influence the behavior of healthcare providers and health policy makers and payers, numerous organizations have developed clinical practice guidelines (CPG) to assist in the diagnosis and treatment of COPD. In such an environment, CPG development often relies upon expert opinion. Conflicting interpretations of the literature regarding COPD management may result in disparities across guidelines. Local factors, such as the availability of certain health care services or the cost impact of an intervention, may also influence how local experts view and apply the published literature during guideline development.

The Global Strategy for the Diagnosis, Management and Prevention of Chronic Obstructive Pulmonary Disease (GOLD) report is one of the most frequently used documents for managing COPD patients worldwide [2,3]. It was developed by using an evidencebased methodology and expert opinion consensus and is considered the most up-to-date, comprehensive reference for COPD diagnosis and management. However, a major gap is that its focus is only in the application of the recommended GOLD strategies for pharmacological treatment of COPD based on the A, B, C, and D groups. Here, our focus of

this survey is to determine the degree of consensus in the Asian Pacific region's practice guidelines for COPD regarding the diagnosis and management of COPD.

#### *Estimated Prevalence*

The prevalence of COPD in the Asia-Pacific countries is estimated at 14.5% in Australia [4], 4.4% to 16.7% in China [5–7], and 5.6% in Indonesia [1]; the prevalence of Air Flow Limitation (FEV1/FVC < 70%) was reported at 10.9% and COPD (after excluding asthmatics) was 8.6% in Japan [8], 13.4% in Korea [9], 4.7% in Malaysia [1], 5.4% to 6.1% in Taiwan [1,10], 3.7% to 6.8% in Thailand [11], 3.5% to 20.8% in Philippines [1,12], and 6.7% in Vietnam [1], respectively (Table 1).

**Table 1.** Publication year of current and last version of Asia Pacific (APAC) guidelines, and COPD prevalence in the reviewed APAC countries.

\* Stepwise management table of COPD was published in 2017; Concise Guide for Primary Care (COPD-X plan) was published in 2017.

#### **2. Method**

A survey was conducted across country-level members of the Asia-Pacific Society of Respiratory (APSR) for collecting an updated version of COPD guidelines which were implemented in each country. The APSR sent a questionnaire to members, who were asked to provide the current local guideline and comparative review of the collected guidelines. Ten guidelines were reviewed, including those of Australia/New Zealand, China, Indonesia, Japan, Korea, Malaysia, Taiwan, Thailand, Philippines, and Vietnam, in either English or national language. The key disease management graphs, flowcharts, and algorithms were translated into English language for review. Detailed information was completely collected, including the definition, the approach to diagnosis, severity classification of staging, pharmacotherapy for stable COPD, and other recommendations. In the Asia-Pacific available COPD guidelines, Australia, Japan, Korea, Taiwan, and China have revised and updated guidelines during the period of 2013 to 2020 (Table 1). Guidelines in the other countries were not revised in the recent three years. We compared the similarities and differences between these guidelines.

The different methods used to estimate disease prevalence including expert opinion, patient-reported diagnosis, and symptom-based or spirometry-based methods may affect the results. In the People's Republic of China, COPD is one of the most common chronic diseases in the population older than 40 years of age, with a prevalence of 8.2% in 2007 and increased to 13.6% in 2015 using spirometry-based survey. [5,7] Comparatively higher prevalence with 13.7% to 13.4% was noted in Korea using spirometry-based survey [9,13]. Another study in the Asia-Pacific region, EPIC Asia population-based survey [1] based on face-to-face or fixed-line telephone interviews, revealed that the prevalence of COPD is between 6.2% and 19.1%. Regarding the estimated prevalence rate of COPD in each country, there is no appropriate method to do this in current status.

#### **3. Results**

COPD diagnosis, classification, and treatment recommendation from Taiwan and China were similar to the GOLD guidelines. The degree of airflow limitation, assessment of COPD severity, management, and pharmacologic therapy of stable COPD were based on the GOLD principles. Australia, Japan, and Korea guidelines display some differences regarding classification and management strategy of stable COPD compared with the GOLD (Table 2). Besides, Taiwan guidelines have been written based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations)'s recommendation, which is the most widely adopted tool for grading the quality of evidence and for making recommendations.

**Table 2.** Comparison of GOLD 2015 and APAC guidelines with current version updated after 2011.

#### *3.1. Combined COPD Assessment*

The Korean COPD guideline categorizes severity into three groups, Group ga (GOLD Group A), Group na (GOLD Group B), and Group da (GOLD Group C and D) [13] (Figure 1). The spirometric cutoff point of FEV1 is 60% predicted to distinct Group ga, na from Group da. They further divide Group da into two groups with FEV1 < 60% predicted, but >=50% predicted, or FEV1 < 50% predicted. [14]. Assessment of symptoms and exacerbation is similar as described in GOLD. In Australia, COPD-X concise guide [15] for primary care categorizes the severity of COPD into mild (FEV1: 60–80% of predicted), moderate (FEV1: 40–59% of predicted), and severe (FEV1: <40% of predicted) accompanied with typical symptoms of varying degree of dyspnea, cough, and limitation of daily activity (Figure 2) [16]. The rationale was that regular treatment with inhaled corticosteroid (ICS) can improve symptoms, lung function, quality of life, and reduce the frequency of exacerbation for patients with FEV1 < 50% predicted and a history of frequent exacerbations, observed in several clinical studies [16–18].

**Figure 2.** Stepwise management of stable COPD guidelines in Australia and New Zealand.

## *3.2. Pharmacologic Management of Stable Disease*

In the GOLD guideline, the initial pharmacological management of COPD is according to patient group which has different recommended treatments. In the guidelines of Australia, Japan, and Korea (Figure 2 [15], Figure 3 [19], and Figure 4 [14]), a stepwise approach of optimized pharmacotherapy for stable COPD is used which recommends a gradual increase of bronchodilators, inhaled corticosteroids, or other drugs based on a comprehensive evaluation of symptoms, airflow obstruction, and exacerbation. In Japan's 2018 guideline, ICS positioning for COPD treatment had been revised from the previous criteria of FEV1 < 50% of predicted, frequent exacerbation, and concomitant asthma to only the concomitant asthma (ACO) criterion.

### *3.3. Non-Pharmacologic Management*

Most guidelines had emphasized the importance of pulmonary rehabilitation, longterm oxygen therapy, and self-management plan including smoking cessation and vaccination. Particularly, Japan's guideline (fifth edition) discussed the nutrition management including nutritional impairment, evaluation, therapy, and diet education [19]. COPD patients whose BMI is less than 90% are suspected to have a nutrition disorder and nutrition therapy may be indicated. Nutritionists, physician, and nurses should form a team to provide nutritional guidance.

**Figure 4.** Algorithm of pharmacologic treatment in patients with stable COPD in Korea.

#### *3.4. Coexisting Asthma and COPD*

Coexisting asthma and COPD are only defined and described in Australia and Japan guidelines. This Australia guideline recommends that an FEV1 increase over 12% and 200 mL constitutes a positive bronchodilator response. An FEV1 increase >400 mL strongly suggests underlying asthma or coexisting asthma and COPD diagnosis. Besides, the diagnosis of asthma–COPD overlap (ACO) has both characteristics of COPD and asthma (Figure 5).

### *3.5. End-of-Life ISSUES*

GOLD 2013, for the first time, proposed that palliative care may be applied in advanced severe COPD patients. Among these guidelines in the Asia-Pacific region, Taiwan, Japan, China [20], and Australia [15] may already have their policies about end-of-life care. Improving quality of life, optimizing function, helping with decision- making about endof-life care, and providing emotional and spiritual support to patients and family are the main goals. In Taiwan, the National Health Insurance Administration Ministry of Health and Welfare had programmed hospice-care plans in 2011 and provided in-hospital critical care facilities for patients with advanced diseases and poor response to regular treatments instead of home or hospice ward care.

**Figure 5.** Diagnostic criteria of asthma–COPD overlap in Japanese ACO guideline 2018.

#### **4. Discussion**

There are several studies evaluating and validating the new GOLD assessment system; however, uneven distribution of COPD patients and limited data on the clinical outcomes are noticed under these combined assessments. [21–24] The degree of the COPD Assessment Test (CAT) score of ≥10 might not be equivalent to that of the mMRC score of ≥2 for categorizing patients' symptoms. [25–28] Neither the 2007 GOLD nor the 2011 classification scheme has sufficient discriminatory power to be used clinically for risk classification to predict total mortality at the individual level. [29] Accordingly, some countries have developed COPD guidelines to build up appropriate strategies for diagnosis, assessment, pharmacotherapy, and prediction of acute exacerbation and mortality based on evidence and real-world clinical practice.

The Korean and Australia guidelines stratified the lung function severity and exacerbation risk with FEV1 < 60% or ≥ 60% of predicted value. From the validation study in Korea, it was found that there were many patients (15.3% to 16%) who experienced exacerbation with FEV1 between 50% and 60% of predicted value. [14] The cutoff point of an FEV1 50% predicted does not address the heterogeneity in the GOLD Stage II (50%–80% predicted). Patients with limited airflow around FEV1 50% to 60% predicted had a more rapid decline in lung function than patients with FEV1 < 50% in the TORCH study [30,31]. A recent study showed that parameters related to volume, diffusing capacity, and reactance showed break-points around 65% of FEV1 which may have an impact on patients' management plan.

The strategy for stable COPD management was based on lung function severity before GOLD 2011. A refinement of the ABCD assessment tool had been separated from spirometric grade from "ABCD" groups in GOLD 2020. A stepwise approach policy is currently presented in the Japan and Australia guidelines. The management strategy is similar in the Korea and GOLD guidelines including for symptoms severity and exacerbation frequency. Moreover, a phenotype-guided treatment policy has been shown in the Spanish and Czech guidelines. [32,33] Which strategies are optimal in clinical practice guidelines for COPD management? There were several strategies including lung function-guided, stepwise approach-guided, GOLD A–D-guided, and phenotype-guided strategies. The optimal treatment of COPD patients requires an individualized, multidisciplinary approach to the

lung function severity, patient's symptoms, clinical phenotypes, biomarkers, comorbidity evaluation, and needs.

The treatment of patients with COPD in a more personalized way must address diverse aspects not only related with the disease, but also with its comorbidities, and current schemes do not offer such personalized medical treatment. Comorbidity evaluation and management were all mentioned in each Asia country CPG. In the JRS guideline l19], the comorbidities included systemic inflammation, osteoporosis, musculoskeletal defect, cardiovascular disorders, gastro-intestinal dysfunction, depression, metabolic disorders, and obstructive sleep apnea. Additionally, the variability of the clinical presentation interacts with comorbidities to form a complex clinical scenario for clinicians. Different comorbidities have different evaluation and management policies. Consequently, the CPG or consensus should be reached over a practical approach for combining comorbidities and disease presentation markers in the therapeutic algorithm, in order to improve the quality of clinical care.

In a previous study, the increased total health expenditure was shown as share GDP ≥ 7% in Korea, Japan, and Australia in 2007. [34] In Japan, major reforms are needed to reduce waste and enhance cost-effectiveness. Moreover, a national system to accredit training programs, including for general practice, has been introduced. [35] The challenges of the healthcare system in Korea include over-consumption and excessively high frequency of specialist consultation, which are major problems for the medical system. The government and the primary care group seek to strengthen primary care, but this is opposed by the medical society governed by the specialist group. [35] In Australia, some provider payment methods were performed such as case payment, diagnostic-related groups, etc. [34]. We think that guideline differences are driven by the disparities in diagnosis modalities or by the treatment variations in different healthcare systems and the socioeconomic burden in each country.

Additionally, diagnosis tools and management of COPD were among the lower guideline-recommended levels in most of the regions investigated among primary care physicians or general practitioners (GPs). [36] The survey demonstrated that the GPs' understanding of COPD was variable and large numbers of GPs have very limited knowledge of COPD and its management in Asia countries. The percentage for COPD management by guideline is as follows: Australia 64%, Japan 74%, Korea 54%, and Taiwan 70%. In China, only 50% of patients with COPD have ever had spirometry tests in tertiary hospitals, and only 18% had in primary or secondary hospitals. [37] Therefore, from the education system, clinical practice, and medical impact, there appears to be an optimal strategy developed to simplify the guidelines for daily practice in each country.

Research evidence has raised concerns that hospital death may be preceded by potentially burdensome and inappropriate hospital admission and aggressive treatments shortly before death, which could be a threat to better end-of-life care and death. [38–41] On the other hand, enabling people to have end-of-life care at home compared with end-of-life care in hospital may incur a potential cost saving. [42,43] The concepts of palliative and hospice care should be established gradually in regards to diseases with an advanced stage.

#### *APSR Recommendations for COPD Diagnosis and Treatment*

#### **5. Conclusions**

This is the first report to summarize the similarities and differences among the COPD guidelines across the Asia-Pacific region. The guideline developed in each country would be based on clinical evidence, experts' consensus, healthcare insurance, reality of clinical practice, and the best interests of patients. We hope, through collaboration of research, that the guidelines will evolve positively and that differences or gaps will diminish with time.

**Author Contributions:** S.-L.C. and C.-H.L.; methodology, validation, and formal analysis; S.-L.C.; writing—original draft preparation, S.-L.C.; writing—review and editing, C.-H.L. Both authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Thanks to the professors for finishing this work including: Kazuto Matsunaga (Japan); Chin Kook Rhee (Korea); Diahn-Warng Perng (Taiwan).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Abstract**

Chemoinformatic analysis was used to characterize a compound database of natural products from Panama and other reference collections. Data mining allowed to compare drug-likeness properties with public and commercial software and to achieve a statistical analysis of the physicochemical properties. Visualization of the chemical space in 3D indicates a high structural similarity. Molecular flexibility and complexity were evaluated using 2D descriptors, whereas the molecular scaffold was obtained using the Murcko method, and these showed few differences between the explored data set. In this chapter, we also present and discuss an example of the application of the chemoinformatic approach using the concept of modeling the activity landscape to study the structure-activity relationships (SARs) of compounds with activity against *Plasmodium falciparum.*

**Keywords:** chemoinformatic, complexity, data mining, physicochemical properties, scaffold

### **1. Introduction**

Natural products (NPs) and their derivatives constitute a significant fraction of approved drugs [1–3], bioactive compounds [4–8], and lead compounds for drug discovery [9]. NP fragment has been used to guide the synthesis of bioactive compounds and generate BIOS combinatorial libraries [10–15]. NPs have structures with different substituent patterns, giving rise to different biological activities for compounds with very similar structures [16–19]. These bioactive metabolites have greater affinity for biological targets and, overall, may have better bioavailability than synthetic compounds, and the presence of pan-assay interference compounds (PAIN) is less frequent in this type of product [20]. The chemoinformatic analysis of several databases of NPs developed by academic institutions and private companies [21] has been carried out in different countries. Thus, the following databases were obtained: BIOFACQUIM [22], CIFPMA [23], NuBBE [24, 25], NANPDB [26], TCM [27], HIT [28], and NPACT [29]. The application of chemoinformatic tools involves the generation, manipulation, and analysis of data set of chemical substances. This allows us through mathematical calculations to order, develop, and evaluate structural information that can be visualized in 2D and 3D [30]. The determination of the physicochemical properties carried out on different databases of NPs and principal component analysis (PCA) was used as an approximation to display the chemical spaces [22–24, 31–37].

**Figure 1.**

*Biological endpoints and targets in which natural products from Panama present bioactivity.*

Computational exploration of NPs has increased in recent years, giving greater relevance to studies that include structural diversity metrics calculated with parameters based on distances such as Euclidean distance, Manhattan distances, and Cosine distance. Other criteria are based on circular fingerprint (ECFP-4, ECFP-6) [22–24, 38–45] and fingerprint based on substructure (MACCS, PubChem) [22–24, 39–45]. Another metric used in NPs is the comparison by similarity that uses the Tanimoto index/Tanimoto coefficient [22–24, 45–49].

In this study, the molecular scaffolds of natural products have been obtained using the Murcko method [22–24, 50–57]. Meanwhile, the molecular complexity is frequently evaluated by descriptors in 2D such as fraction of sp3 hybridized carbons (Fsp3 ) [23], fraction of chiral centers (FCC) [23], and globularity [22–24, 58–63].

An update of the Natural Products Database from the University of Panama (UPMA) containing 454 compounds (Unpublished data) has been evaluated against different therapeutic targets such as cytotoxicity bioassay in cell lines, antifungal assay in vitro, parasites of tropical diseases (*Leishmania* sp., *Plasmodium falciparum*, and *Trypanosoma cruzi*), and the bioassay against HIV-1 virus, demonstrating an inhibitor effect on protease, reverse transcriptase, nuclear factor NFkappaB, and Tat protein affecting the viral replication. These are the most significant biological targets in which the natural products from Panama present bioactivity. The values of their biological activities are represented as percentages in **Figure 1**.


**6. Effects of the volcano eruptions**

10 Atmospheric Aerosols – Regional Characteristics – Chemistry and Physics

**Figure 5.** Time variations in the annual values of AOD and in the flux of direct solar radiation for the Sun's height 30°: (a) multiyear variations in the annual values of AOD for three stations (Krasnodar (1), Chita (2), and Okhotsk (3)) and (b) multiyear variations in the annual values of AOD and in the annual mean of direct solar radiation flux at the Sun's height 30° for the two stations with the maximum and minimum means of AOD. For both graphs, the period under analysis is 1976– 2010. Krasnodar(*1*  corresponds to AOD and 3corresponds to direct radiation), Solyanka ( *2* corresponds to AOD and *4*

(b)

(a)

corresponds to direct radiation)

## **6.1. Influence of the volcano eruptionson AOD**

Fig. 7 gives a "long" (45 years) series of annual means of AOD for the Ust' Vym station (62.2°N, 50.4°E), which demonstrates a characteristic multiyear trend of variations in the annual values of AOD and its response to stratospheric disturbances. The four powerful volcanic episodes— Agung ( 8°S, 116°E, 1963), Fuego (14°N, 91°W, 1974), El Chichon (17°N, 93°W, 1982), and Pinatubo (15°N, 120°W, 1991)—are clearly pronounced and quantitatively estimated. In particular, the maximum effect observed a year after the eruptions is 100% (in deviations from the multiyear norm); throughout the year, its attenuation occurs with the dissipation and transformation of the stratospheric aerosol layer. A decrease in the AOD values for 1995–2006 is also clearly manifested. Such a character of multiyear variations in the annual values of AOD is characteristic of most stations and is, to a great extent, determined by the four powerful volcanic eruptions in the latter half of the 20th century, because seasonal and local disturbances caused by the effects of tropospheric aerosol, when annually averaged, become leveled and have almost no influence on the distribution of the multiyear values of AOD.

Variations in the Aerosol Optical Depth Above the Russia

from the Data Obtained at the Russian Actinometric Network in 1976–2010 Years 13

ΔТ2%=100\*(Ti-Tm)/Tm ΔТ2= (Ti-Tm)/σ El Chichon Pinatubo El Chichon Pinatubo

T (1976-2005) **/** Tstab (1976-2005)

North of EPR 20 32 1.9 3.0 Central part of EPR 18 30 1.8 3.1 Southof EPR 14 20 1.8 2.6 Ural 26 23 2.6 2.3 West Siberia 19 35 1.7 3.1 North–east of APR 19 35 1.7 3.2 Central part of APR 19 27 2.0 2.8 Southof EPR 22 38 1.8 3.0 Far East of the Russia 15 36 1.5 3.4 **Table 2.** Anomalies of the mean month values of the Т during post volcanic period after the eruptions

Month 1 2 3 4 5 6 7 8 9 10 11 12 Year North of EPR 1,03 1,03 1,04 1,03 1,03 1,02 1,02 1,03 1,02 1,04 1,03 Central part ofEPR 1,04 1,05 1,04 1,04 1,03 1,02 1,02 1,02 1,01 1,02 1,02 1,05 1,03 Southof EPR 1,04 1,04 1,03 1,03 1,02 1,02 1,01 1,01 1,01 1,01 1,03 1,04 1,02 Ural 1,05 1,06 1,06 1,04 1,03 1,03 1,02 1,02 1,03 1,02 1,05 1,05 1,04 West Siberia 1,03 1,05 1,04 1,03 1,04 1,03 1,02 1,03 1,04 1,03 1,02 1,02 1,03 North–east ofAPR 1,07 1,07 1,04 1,03 1,03 1,03 1,03 1,02 1,02 1,03 1,05 1,07 1,04 Central part ofAPR 1,04 1,02 1,02 1,02 1,02 0,98 0,98 1,02 1,01 1,00 1,06 1,04 1,02 Southof EPR 1,06 1,04 1,04 1,04 1,03 1,02 1,02 1,02 1,02 1,02 1,03 1,03 1,03 Far East of the Russia 1,06 1,05 1,04 1,03 1,02 1,02 1,01 1,01 1,01 1,02 1,03 1,05 1,03

Month 1 2 3 4 5 6 7 8 9 10 11 12 Year North of EPR 1,11 1,10 1,10 1,08 1,07 1,07 1,06 1,06 1,06 1,14 1,07 Central part of EPR 1,06 1,07 1,08 1,09 1,10 1,08 1,08 1,07 1,06 1,02 1,09 1,10 1,07 Southof EPR 1,10 1,09 1,06 1,07 1,05 1,04 1,03 1,02 1,03 1,04 1,10 1,14 1,06 Ural 1,08 1,12 1,14 1,11 1,09 1,08 1,07 1,08 1,14 1,08 1,07 1,08 1,06 West Siberia 1,05 1,10 1,11 1,09 1,11 1,10 1,08 1,06 1,08 1,13 1,07 1,08 1,08 North–east of APR 1,03 1,13 1,11 1,08 1,09 1,08 1,08 1,07 1,09 1,11 1,13 1,02 1,09 Central part of APR 1,08 1,07 1,08 1,09 1,10 1,08 1,08 1,07 1,06 1,05 1,13 1,11 1,06 Southof EPR 1,09 1,08 1,09 1,10 1,08 1,05 1,05 1,05 1,05 1,04 1,06 1,06 1,07 Far East of the Russia 1,12 1,11 1,09 1,06 1,06 1,07 1,06 1,03 1,04 1,04 1,08 1,12 1,07 **Table 3.** Estimation of the volcano contribution into the multiyearmean values of the factor turbidity and aerosol optical depth during 1976 - 2005 years period; EPR -- the European Part of Russia; APR --

AOD (1976-2005) /AODstab.(1976-2005)

Region

of El Chichon and Pinatubo.

theAsian Part of Russia.

**Figure 7.** Example of multiyear variations in the annual means of AOD (red) and their deviations from the averaged (blue),

d *=*100%\*(AODi - AODm) / AODm.

## **6.2. Influence of the volcano eruptions on the turbidity factor (T)**

From the data of 80 observation stations over the Russia the special analysis of the turbidity factor (T) have been fulfilled: time variations during 1976-2010 y.y. and during 1994-2010 y.y. have been estimated. For the 9 regions over all Russia territory long-term trends for the characteristics of the integral atmospheric transparency have described. For all regions during 1976-2010 y.y. negative Т and AODvariations tendencies exist; during 1994-2010 y.y. negative Т2 иАОТ variations tendencies remain at the same level as during 1994-2009 y.y. practically for all Russia regions. So, for the most part of Russia territory the conditions of the relatively high atmospheric transparency (in 1994-2010 y.y. – 17 years) remain as well as the atmospheric transparency increase within this 17 years time interval remain. Comparatively stable, longterm and intensive variations (increase) take place in postvolcanic periods: 1) for El Chichon eruption (1982 year, April) – from the last 1982 year to October of 1983 year; 2) for Pinatubo (1991 year, June) – from the September of 1991 year to July of 1993 year. Anomalies of the mean month values of the Т2 during these "post volcanic" period after the eruptions of El Chichon and Pinatubo are presented in Table 2.

Estimations of the volcano contribution into the multiyear mean values (for the months and year) of the factor turbidity and aerosol optical depth during 1976 – 2005 years period and durings the so called "stable" 1976-2005 years period (without 1982, 1983, 1991,1992, 1993 years) are pointed in Table 3. It is obvious that effects, connected with eruptions lead to increase of the multiyear mean values equal 3% (from 1% - to 7%) for Tand equal 7% (from 2% - to 12%) for AOD.

Variations in the Aerosol Optical Depth Above the Russia from the Data Obtained at the Russian Actinometric Network in 1976–2010 Years 13

12 Atmospheric Aerosols – Regional Characteristics – Chemistry and Physics

the averaged (blue),

2% - to 12%) for AOD.

**Figure 7.** Example of multiyear variations in the annual means of AOD (red) and their deviations from

d *=*100%\*(AODi - AODm) / AODm.

From the data of 80 observation stations over the Russia the special analysis of the turbidity factor (T) have been fulfilled: time variations during 1976-2010 y.y. and during 1994-2010 y.y. have been estimated. For the 9 regions over all Russia territory long-term trends for the characteristics of the integral atmospheric transparency have described. For all regions during 1976-2010 y.y. negative Т and AODvariations tendencies exist; during 1994-2010 y.y. negative Т2 иАОТ variations tendencies remain at the same level as during 1994-2009 y.y. practically for all Russia regions. So, for the most part of Russia territory the conditions of the relatively high atmospheric transparency (in 1994-2010 y.y. – 17 years) remain as well as the atmospheric transparency increase within this 17 years time interval remain. Comparatively stable, longterm and intensive variations (increase) take place in postvolcanic periods: 1) for El Chichon eruption (1982 year, April) – from the last 1982 year to October of 1983 year; 2) for Pinatubo (1991 year, June) – from the September of 1991 year to July of 1993 year. Anomalies of the mean month values of the Т2 during these "post volcanic" period after the eruptions of El Chichon and Pinatubo are presented in Table 2.

Estimations of the volcano contribution into the multiyear mean values (for the months and year) of the factor turbidity and aerosol optical depth during 1976 – 2005 years period and durings the so called "stable" 1976-2005 years period (without 1982, 1983, 1991,1992, 1993 years) are pointed in Table 3. It is obvious that effects, connected with eruptions lead to increase of the multiyear mean values equal 3% (from 1% - to 7%) for Tand equal 7% (from

**6.2. Influence of the volcano eruptions on the turbidity factor (T)**

**Table 2.** Anomalies of the mean month values of the Т during post volcanic period after the eruptions of El Chichon and Pinatubo.

**Table 3.** Estimation of the volcano contribution into the multiyearmean values of the factor turbidity and aerosol optical depth during 1976 - 2005 years period; EPR -- the European Part of Russia; APR - theAsian Part of Russia.

The examples of the long-term time variations for Tand AOD in the differentRussia regions:North,Central part and South of theEuropean Part of the Russia andRussianFar East are presented in Fig. 8.

Variations in the Aerosol Optical Depth Above the Russia

from the Data Obtained at the Russian Actinometric Network in 1976–2010 Years 15

The events of summer 2010 (abnormal heat and forest and peat fires) evidently changed both the average values of air turbidity and the character of its spatial variations. Therefore, our estimates are of interest in the analysis (All Russia Meeting, 2010) of the situation on the European Part of Russia (EPR) in summer 2010. Fig. 9 presents the coordinates of solar radiometry stations on the EPR (Luts'ko et al., 2001); data from it were used in this work. The long-term annual average (over a "post-volcanic" period of 1994–2009 years) values *Т*postfor summer months and the corresponding monthly values *Т*2010for 2010 are given in Table 4, along with the monthly average maxima of *Т*and the relative difference (%) *D* = (*Т*2010 – *Т*post)/*Т*post. As it is seen, the average July and August *Т*in 2010 and in the "postvolcanic" period differ by –6% and +4%, respectively (the differences *D* vary from – 28% to +11% of the average value for a certain station in June and from – 22% to +25% in July). The value of *D* = (*Т*2010 – *Т*post)/*T*postis 14% in August (for the region) and varies from –

**Figure 9.** Layout of 18 actinometric stations on the EPR whose data will be analyzed in this section.

Spatial variations in Тare shown in Fig. 10. To interpolate the data of the stations to the whole region under study , we also used features of the MATLAB package, i.e., the option for creating a homogeneous grid for the EPR region under study, the option of bilinear (horizontal and vertical) interpolation of data from 18 stations to the territory (40°–70° N, 30°–60° E), and the projection of the function Т*=* F(ϕ, λ) (where ϕand λare the longitude and latitude, respectively, for each of the observational points) to the grid. The spatial distribution of the mean Tpost(for June, July, and August) for the "postvolcanic" period corresponds to the results obtained earlier (Plakhina et al., 2009) for the long-term annual average AOD. In this period, Tpostquasi -monotonically decreased from southwest to

11% to +48% for certain stations.

**Figure 8.** Examples of the long-term time variations for T(blue)and AOD (green) in the different Russia regions: South of theEuropean Part of the Russia (1)and North part (2).