Interactive Visualisation of the Profitable Amount of Waste to Dispose Illegally

"Wow!" - I said to myself after reading R Helps With Employee Churn post - "I can create interactive plots in R?!!! I have to try it out!"

I quickly came up with an idea of creating interactive plot for my simple model for assessment of the profitable ratio between the volume waste that could be illegally disposed and costs of illegal disposal [Ryabov Y. (2013) Rationale of mechanisms for the land protection from illegal dumping (an example from the St.-Petersburg and Leningrad region). Regional Researches. №1 (39), p. 49-56]. The conditions for profitable illegal dumping can be describes as follows:

Here: k - the probability of being fined for illegal disposal of waste;
P - maximum fine for illegal disposal of waste (illegal dumping);
V - volume of waste to be [illegally] disposed by the waste owner;
E - costs of illegal disposal of waste per unit;
T - official tax for waste disposal per unit.

The conditions for the profitable landfilling can be described as follows:

Here: V1 - total volume of waste that is supposed to be disposed at illegal landfill;
Tc - tax for disposal of waste at illegal landfill per unit;
P1 - maximum fine for illegal landfilling;
E1 - expenditures of the illegal landfill owner for disposal of waste per unit.

Lets plot the graphs (with some random numbers (except for fines) for a nice looking representation) to have a clue how it looks like.

Note that there is a footnote (this post provides nice examples on how to do it) with the values used for plotting - it is important to have to have this kind of indication if we want to create a series of plots.

Now I will show you the result and then will provide the code and some tips.

Playing with the plot

Tips and Tricks

Before I will show you code I want to share my hardly earned knowledge about nuances of the manipulate library. There are several ways to get static plot like that using ggplot, but some of them will fail to be interactive with manipulate.

1. All the data for the plot must be stored in one dataframe.
2. All data for plots must be derived from the dataframe (avoid passing single variables to ggplot).
3. Do not use geom_hline() for the horizontal line - generate values for this line and store them inside dataframe and draw as a regular graph.
4. To create a footnote (to know exactly which parameters were used for the current graph) use arrangeGrob() function from the gridExtra library.
5. Always use $ inside aes() settings to address columns of your dataframe if you want plots to be interactive

The Code

library(ggplot2)
library(grid)
library(gridExtra)
library(manipulate)
library(scales)
library(reshape2)

## Ta --- official tax for waste utilisation per tonne or cubic metre.
## k --- probability of getting fined for illegal dumping the waste owner (0 V, y <=> E

max_waste_volume <- 2000 
Illegal_dumping_fine_P <- 300000
Illigal_landfilling_fine_P1 <- 500000
Fine_probability_k <- 0.5
Official_tax_Ta <- 600

# mwv = max_waste_volume
# P = Illegal_dumping_fine_P
# P1 = Illigal_landfilling_fine_P1
# k = Fine_probability_k
# Ta = Official_tax_Ta

updateData <- function(mwv, k, P1, P, Ta){
    
    # creates and(or) updates global data frame to provide data for the plot
    
    new_data <<- NULL
    new_data <<- as.data.frame(0:mwv)
    names(new_data) <<- 'V'
    new_data$IlD <<- k*P1/new_data$V
    new_data$IlD_fill <<- new_data$IlD
    new_data$IlD_fill[new_data$IlD_fill > Ta] <<- NA # we don't want ribbon to fill area above Official tax 
    new_data$IlL <<- Ta-k*P/new_data$V
    
    new_data$Ta <<- Ta
    new_data$zero <<- 0
    dta <<- melt(new_data, id.vars="V", measure.vars=c("IlD", "IlL", "Ta"))
    dta.lower <<- melt(new_data, id.vars="V", measure.vars=c("IlD_fill", "zero", "Ta"))
    dta.upper <<- melt(new_data, id.vars="V", measure.vars=c("Ta", "IlL", "Ta"))
    dta <<- cbind(dta, lower=dta.lower$value, upper=dta.upper$value)
    dta$name <<- factor(NA, levels=c("Illegal landfill owner's\nprofitable ratio",
                                    "Waste owner's\nprofitable ratio", 
                                    "Official tax"))
    dta$name[dta$variable=="IlD"] <<- "Illegal landfill owner's\nprofitable ratio"
    dta$name[dta$variable=="IlL"] <<- "Waste owner's\nprofitable ratio"
    dta$name[dta$variable=="Ta"] <<- "Official tax"
}

updateLabels <- function(k, P1, P, Ta){
    
    ### creates footnote caption for the plot
    
    prob <- paste('Fining probability = ', k, sep = '')
    landfilling_fine <- paste('Illegal landfilling fine = ', P1, sep = '')
    dumping_fine <- paste('Illegal dumping fine = ', P, sep = '')
    tax <- paste('Official tax = ', Ta, sep = '')
    note <<- paste(prob, landfilling_fine, sep = '; ')
    note <<- paste(note, dumping_fine, sep = '; ')
    note <<- paste(note, tax, sep = '; ')
    note
}


plotDumping <- function(mwv, P, P1, k, Ta){
    
    ### this function draws the plot
    
   # initialise plot data
    updateData(mwv, k, P1, P, Ta)
    updateLabels(k, P1, P, Ta)
    
    # draw the plot
    profit <- ggplot(dta, aes(x=dta$V, y=dta$value, ymin=dta$lower, ymax=dta$upper, 
                              color=dta$name, fill=dta$name, linetype=dta$name)) +
        geom_line(size=1.2) + 
        geom_ribbon(alpha=.25, linetype=0) +
        theme(axis.text.x = element_text(angle=0, hjust = 0),
              axis.title = element_text(face = 'bold', size = 14),
              title = element_text(face = 'bold', size = 16),
              legend.position = 'right',
              legend.title = element_blank(),
              legend.text = element_text(size = 12),
              legend.key.width = unit(2, 'cm'),
              legend.key.height = unit(1.2, 'cm'))+
        scale_linetype_manual(values=c(4, 5, 1)) +
        scale_fill_manual(values = c("#F8766D","#00BFC4",NA)) +
        scale_color_manual(values = c("#F8766D","#00BFC4", '#66CD00')) + 
        labs(title="Profitable ratio between the volume \nof illegally disposed waste \nand costs of illegal disposal of waste",
             x="Waste volume, cubic meters",
             y="Cost per cubic meter, RUB") +
        xlim(c(0, max(new_data$V)))+
        ylim(c(0, Ta*1.5))
        
    
    # add a footnote about paramaters used for the current plot
    profit <- arrangeGrob(profit, 
                          sub = textGrob(note, 
                                         x = 0, 
                                         hjust = -0.1, 
                                         vjust=0.1, 
                                         gp = gpar(fontface = "italic", fontsize = 12)))
    
    # show plot
    print(profit)

}


simDumping <- function(max_waste_volume = 2000, 
                       Illegal_dumping_fine_P = 300000,
                       Illigal_landfilling_fine_P1 = 500000,
                       Fine_probability_k = 0.5,
                       Official_tax_Ta = 600) {
    
    ### this function creates interactive plot
    
    max_waste_volume <<- max_waste_volume 
    Illegal_dumping_fine_P <<- Illegal_dumping_fine_P
    Illigal_landfilling_fine_P1 <<- Illigal_landfilling_fine_P1
    Fine_probability_k <<- Fine_probability_k
    Official_tax_Ta <<- Official_tax_Ta
    
    manipulate(suppressWarnings(plotDumping(max_waste_volume, 
                           Illegal_dumping_fine_P,
                           Illigal_landfilling_fine_P1,
                           Fining_probability_k,
                           Official_tax_Ta)
                           ),
               
               # set up sliders
               max_waste_volume = slider(0, 50000, 
                                         initial = max_waste_volume, 
                                         step = 100,
                                         label = 'X axis range'),
               Illegal_dumping_fine_P = slider(0, 5000000, 
                                               initial = Illegal_dumping_fine_P,
                                               step = 10000, 
                                               label = 'Illegal dumping fine (P)'),
               Illigal_landfilling_fine_P1 = slider(0, 5000000, 
                                                    initial = Illigal_landfilling_fine_P1, 
                                                    step = 10000,
                                                    label = 'Illegal landfilling fine (P1)'),
               Fining_probability_k = slider(0, 1, 
                                             initial = 0.5,
                                             step = 0.01,
                                             label = 'Probability of being fined (k)'),
               Official_tax_Ta = slider(0, 3000, 
                                        initial = Official_tax_Ta, 
                                        step = 50,
                                        label = 'Official waste disposal tax (T)')
    )
}

simDumping() # for reasons unknown I have to run this function twice to get proper interactive plot

Schema of the Conservation Areas in Leningradskaya Region: Some Notes for Beginner Mappers

Schema of the Conservation Areas in Leningradskaya Region

About a year ago I was asked to create a small (a B5 size) and simple schema of the conservation areas in Leningradskaya region. I did it using QGIS. Here you are the author version of the schema and several notes that might be helpful for a beginner map-maker.

There was a huge disproportion between areas of different objects and both polygon and point markers were needed to show them. I decided to use Centroid Fill option in polygon style to be able to use only one layer (polygon) instead of two (polygon and point). Using Centroid Fill makes points in centres of the small polygons overlap and mask these tiny polygons. 

All the administrative borers were stored in one layer (and there are far more borders than one see here). They are drawn using rule-based symbology so I didn't even need to subset this layer to get rid of the rest of the polygons in this layer.

All the names of the surrounding countries, regions, city and the water bodies are not labels derived from layers, but labels created inside the map composer. It was quicker and their position was more precise which is crucial for such a small schema.

There was a lack of space for the legend so I had to utilise every bit of canvas to place it. I had to use 3 legend items here. One of them is actually overlapping the other and setting a transparent background for the legends was helping with that.

Finally labels for the conservation areas (numbers) were outlined with white colour to be perfectly readable. Some of them were moved manually (with storing coordinates in special columns of the layer) to prevent overlapping with other labels and data.

P.S. Don't be afraid to argue with the client about the workflow. Initially I was asked it manually digitise a 20 000 x 15 000 pixels raster map of the Leningrad region to extract the most precise borders of the conservation areas (and districts of the region). Of course I could do it and charge more money for the job, but what's the point if some of that borders are not even to be seen at this small schema? So I convinced client to use data from OSM and saved myself a lot of time.

Fire Dynamics In Leningrad Region in 2006

Today I decided to play with TimeManager plugin for QGIS that was introduced by underdark some time ago. After some search across my spatial storage I found only one dataset that contained timestamps: FIRMS fire data that I used for creation of a methodology for burn-out probability calculation (for illegal dumping environmental risk assessment) and for assessment of human influence on fire starting.

Seems that TimeManager is not quite stable after the certain number of features in layer. At least on my machine it crushed several times (during slider manipulation) on the whole dataset of about 7000 points but worked just fine with its one-year subset of about 2700 points. But anyway overall impression is very good.

The main issue was to create a video from the generated .png files. I used console ffmpeg utils. Simple command:
:~>  ffmpeg -r 1/1 -i frame%03d.PNG -vcodec yuv420p -video_size 1126x560 output.flv
produced a 3-minute video above. One second corresponds to 1 day of the most  flammable year for the Leninngrad region in a decade. Video covers period from April to November 2006. I was too lazy to create custom undercover and just loaded OSM via OpenLayers plugin)))

Spatial Randomness Evaluation in R: Monte Carlo Test

This post is a some kind of reply to this one.

So our goal is to determine whether our point process is random or not. We will use R and spatstat package in particular. Spatstat provides a very handy function for this, that uses K-function combined with Monte Carlo tests. I will spear you from burbling  about theory behind it - the necessary links were already provided. Lets get directly to action.

In this example I will test data about location of my "favourite" illegal dumps in St. Petersburg and Leningrad region.

# we will need: 

library(maptools) 

library(rgdal) 

library(spatstat)

# import data for analysis

S <- readShapePoints("custom_path/dump_centroids.shp", proj4string= CRS("+proj=tmerc +lat_0=0 +lon_0=33 +k=1 +x_0=6500000 +y_0=0 +ellps=krass +towgs84=23.92,-141.27,-80.9,-0,0.35,0.82,-0.12 +units=m +no_defs"))

SP <- as(S, "SpatialPoints")

P <- as(SP, "ppp")

# perform the test itself with a 100 simulations

E <- envelope(P, Kest, nsim = 100)

plot(E, main = NULL)

And here is what we've got in the end, a fancy graph, which demonstrates that our data (Kobs) significantly deviates from a random process (Ktheo):

Illegal Dumping in Leningrad Region: Press Conference

There was a press conference on Tuesday the 19-th about illegal dumping in Leningrad region (Russia). I was asked to be the main speaker there and to present to the press my recent study on illegal dumping prevention. I've already had two presentations on this subject recently at the international scientific conference in St. Petersburg State University and at the round table for the discussion of the upcoming "Let's do it. Russia" clean up event.

Some video from the press conference:

The main conclusion that I made by investigating possible impacts on illegal dumping prevention (such as penalty increase, chance of being caught increase and waste disposal fare decrease) is that decrease of the waste disposal fare for population is the most efficient way. And I managed to find two other publications that came to the exact conclusion (for example, there is an evidence that 1% waste fare increase leads to 3% increase of illegal dumping cases).

By the way I was able to assess probability of being caught for illegal dumping in Russia. It is about 10^-5 (you can die while playing soccer with such probability).

The only way to reduce waste fares is to use waste as a resource. That means that the only way to prevent illegal dumping is to create waste management system that would be able to complete the zero waste goal.

And here is an abstract from my article:

Mechanisms of the land protection were discussed in this article. An algorithm of decision making whether to dump illegally or not was explained. Formulas for determination of profitable ration of expenditures per unit and amount of illegally dumping waste are substantiated. Effect from different types of impacts that can be used for land protection from illegal dumping were discussed (such as fares change, penalties change, penalty application probability change). Decreasing of waste disposal fares was acknowledged as the most effective way for illegal dumping prevention, but it is possible only if «zero waste» concept is implemented.

Relation Between Fires and Distanse to the Nearest Road (Recalculated)

As you may already know, I'm a proud owner of AMD FX-8150 8-core CPU. And I've purchased it not for gaming reasons, but for science. My previous CPU was painfully slow with such calculations as determination of the relation between fires and distance to the nearest highway. I even didn't try to perform that calculations on the whole dataset of the roads mapped in OSM in Leningrad region. But now I can do this!

With the new CPU I've recalculated previous distribution (with the same data) in dependence only on highways and performed new calculation on the whole roads dataset. Some numbers first: 

• 6,990 - number of fire points detected by FIRMS for the last 10 years in Leningrad region;
• 10,966 - number of the highway features used as highways for calculations;
• 87,422 -number of features from whole dataset of roads;
• 2,3 Gb RAM and a single core were consumed by R during calculations for the whole dataset.

Results:

Recalculated fire distribution for the highways

Recalculated values for the highways are different to the acquired at the last time despite the data was the same. But there were hardware update and most important - software updates for R and its packages (OS was updated too). But this graph looks far more reasonable than the previous one.

Lets see what we've got for the whole roads dataset (I will compare it to the graph above).

Distribution calculated for the whole dataset of roads

The maximum distance from road decreased almost in to times: from 41 to 26 kilometres. The distance for the highest values decreased accordingly: a rapid decreasing stops at 7 kilometres and for only highways it was 18 kilometres.

So the first 5 kilometres from the road are the most probable zone fore the fire event. This distance is easily covered on foot in two hours. Another evidence of the massive anthropogenic impact on fire starting.

If I will ever lay hands on the road data from the topographic maps (here OSM data used) I will perform the calculation again to get the most precise data.

Conclusion: FX-8150 worth buying )))

Illegal Dumping and Cadastre

As a specialist in land monitoring and cadastre I always suspected that uncertainty in legal status of the land parcel may provoke illegal dumping at such parcel. One evidence I collected during the study on implementation of high resolution imagery for monitoring of illegal dumping - illegal landfill occurred on the land parcel with no particular legal status. Today I found another one - illegal dump existed for several years because the land parcel owner wasn't determined.

Relation Between Fires and Distanse to the Nearest Highway

Instead of introduction

Just for fun I decided to investigate relationship between fires intensity in Leningrad region (and St. Petersburg as well) and distance to the nearest road in order to gain the evidence of the major influence of the anthropogenic factor on fire starting.

Materials and methods

Data used:

• FIRMS fire data;
• OSM data from GIS-LAB.info.

Software used: QGIS; R.

OSM data about major roads of Leningrad region was used to create a distance map. Distance map and data about locations of the fires detected from 2001 to June 2011 were used as arguments for the R "rhohat" function of "spatstat" package in order to investigate dependence of fires intensity on distance to the nearest highway.

Results and discussion

Firstly lets look at the map of the fires intensity distribution in space below (this map is rough and was created just to demonstrate the situation in general and it wasn't used for the computations described below). Due to usage of the roads at this map will be a hinder for fire data I used railways instead.

Rough fires intensity distribution in Leningrad region

As you can see, fires intensity is [somewhat] related to the location of the railways: note that there are almost no fires at the east side of the map where railways net is sparse (north side of the map is similar, but there is Ladoga Lake located). Ofcourse railways are not the cause for the fires by themselves, but I suppose that in this particular case this can be evidence of the significant human influence on the fire events.

I believe you think that if we want to find clear evidence of the human influence on starting fire than we should investigate density of the population and compare it to the fires intensity. Fair enough, but you may do it by yourself using official data if you want. I will not do it because the results will be flawed. There is an issue with  population - there is no data for the actual population of Leningrad region in summer (late spring and early autumn as well) time - the time of fires. At this time a lot of people from St.Petersburg go to their summer houses in Leningrad region (take into account that the population of St. Petersburg is 5 times higher than a population of Leningrad region) and you have to estimate population of the region accordingly, and it is far not a trivial task.

So we have to investigate not the population density by itself, but the proximity of the areas that were on fire to the population. We will measure the proximity as a distance to the nearest highway. Ofcourse it is better to use all available roads for such research, but if we will do so, R will calculate it for a very long time (there are over 80 000 features in the shp-file) - I've stopped the process after 10 hours of waiting. So I've used only major highways (with "primary", "secondary", "trunk", "tertiary" and "motorway" attributes in OSM) - over 10 000 features.

rhohat{spatstat} function was used to create the following graph:

Dependence of the fires distribution on the distance to the nearest highway

The graph is somewhat weird (and we will talk about it) but it is obvious that intensity of fires have maximum at 2-3 kilometres from the highway and then goes down. So we have the piece of evidence that anthropogenic factor has a major influence on the fire events indeed.

There is an interesting "sinusoid" from 18th to 38th kilometre. Narrow gray stripe demonstrates possible error so I assume that this graph is reliable. I need to explain it somehow. My wife who is better mathematician than I tells that the best explanation is a shitty computation. Well, it is possible. But I have another opinion.

Possible explanations:

1. Remember that we left more than 70 000 roads outside our calculations for this graph and there are a lot of roads which are not recorded in OSM. I suppose that if we would use almost complete dataset of roads in Leningrad region and more powerful computer than mine we would have more adequate graph where this sinusoid may disappear.
2. Proximity, estimated here is not only a proximity for "occasional fire starters" but also a proximity for fire fighters. If a fire starts far away from the road it is more difficult to fight it. So this is about fire data and how we treat it for the computation: it is possible that fire events are continuously less frequent as we move away from the highway, but single event produce fire which covers larger area than average due to it is hard to fight it quickly, and this single event produce more "hotspots" (which were used for this mini-research) than average event.

Conclusions

We have got a strong evidence that anthropogenic factor plays a major role as cause for fire in Leningrad region: intensity of fires riches its maximum at distance of 2-3 km away from the road and then goes down.

"Sinusoid" between 18th and 38th kilometres may be caused by insufficiency of the road data used. Calculations should be repeated with more comprehensive data. Also it may be necessary to pre-process fire data in order to replace "hotspots" related to a single fire event with the single point i.e. to make it one point for one fire.

UPD: the story continues here.

FIRMS for Detection of Fires at Illegal Dumps

Today I've acquired another evidence that FIRMS data can be used for monitoring of fires at illegal dumps. Of course I was sure it is possible and I was able to detect fires occurred at one of the landfills in St. Petersburg, but I had no evidences that fires occurred next to illegal dumps with known [to me] location were indeed fires at illegal dumps, because I do not have an information about dumps age and FIRMS accuracy about 1 km and exceeds common size of dump, which is about couple of dozens or hundreds of meters, so there is no way I could insist that fire points next to illegal dumps were actually fires at illegal dumps.

But I've found some good (actually - bad) news today. There is a note about fires at two illegal dumps in Leningrad region. Look what we've got:

Despite no points located at the illegal dumps directly they lie within 1 km zone from the borders of dumps and the news show that these fires are actually dump fires. The bad thing is that these fires lasts for a several days, but were detected just one and two times.

3 cool events of the week

There were 3 very interesting events on this week (during my official vacation):

• a Russian - Finnish workshop dedicated to our project related to management of hazardous part of household waste.
• An "International" conference named "An Environmental Equilibrium"; and...
• OSM mapping party in Volhov town.

A workshop passed as always: Russians posed themselves as know-all persons (despite no one was prepared because all the materials were in English...), talked a lot but ineffective; and Finnish did the real job. For the first time in my life I had to be a parallel Russian-Finnish and a Finnish-Russian translator. A hard job, I have to admit...

"An Environmental Equilibrium" conference (which first day I missed because of the workshop) exceeded my expectations... a little, actually, but nevertheless. The funniest thing was that the first face I saw when entered the lecture hall was the face of my scientific adviser, who new that I will participate but didn't tell me that he will be a participant too O_o. He leaved quickly so we were unable to talk.

There were not as many people as expected (because of the summer time and a poor feedback from organisers, who didn't provided participants with the time table in advance, I suppose the main part of scientists who sanded papers preferred to be somewhere else) - organisers even had to merge together all the sections because of the lack of speakers. The good thing was that the publications were ready and I was able to enjoy my article on the subject of illegal dumping monitoring at St. Petersburg with implementation of high-resolution satellite imagery. The bad thing was that my scientific adviser had already left the conference when I demonstrated my presentation.

An unexpected finding was that the map in my Garmin, which expected to be fresh, had a poor an outdated information about location of the conference and OSM was also poor, but up to date.

OSM mapping party in Volhov was my first mapping party. Despite there were few  participants (actually if there were more of them I wouldn't be able to participate at all because in that case party would took place a week earlier, when I was on vacation in Narva... and mapped it; collected data became more important due to imagery for Narva won't be available soon) and heavy rain (from time to time) we were able to survey western part of town.

After 2 hours of mapping we gathered at the cafe to discuss our survey. I've made an attempt to find an answer for the the question about mapping managed green areas, but it seems that I'm the only one who care.

The most unpleasant part was an attention from drunken ex-prisoner in cafe, who started to ask questions about our activities and then begged for money for beer. In Narva my wife and I brought attention of the local drunkards while survey too...

In the end we had a nice attraction on board of Zverik's jeep - a survey of the Russian roads ;-)

At the Seminar for waste utilisation in St. Petersburg and Leningrad region

This seminar organised by "St. Petersburg Recycling Association" occurred the other day. Heretofore I thought that this Association is a batch of ass-kissers (Eng), but I was wrong - they are just conformists (in presence of these in power they are posing respect to their opinion on the subject of waste management development, but when these in power are not around they speak more sincerely). There were three most interesting persons to me at the seminar: Vaisberg (Eng) (head of the Accociation and Mekhanobr Corporation), Hmirov (Eng) (head of the waste management department of St. Petersburg) and a deputy from Leningrad region whose name I wrote down but was unable to read it because I write a very bad hand. Here they are (sorry for the poor quality).

From left to right: Vaiseberg, Hmirov and deputy in the pink shirt

I would like to share the main points of their speeches dedicated to waste problems in St. Petersburg and Leningrad region.

Hmirov:

1. There is a need to develop legislation to make producers co-finance waste treatment.
2. Waste treatment payments should be accumulated at municipal and regional level instead of management companies [it is interesting that hi said that payments should be accumulated in people's not official's hands - LOL, such a stupid hypocrisy].
3. Municipal solid waste (MSW) have to be used as a power source [he is known for standing for waste to energy technologies]. And there is even some Swedish company already interested in buying RDF which would be produced after new waste plants will be constructed.
4. Local folks have a "lack of understanding" [I'm citing] all the importance of the new "waste-processing complex" [ironical quotes] construction and are about to ruin (Eng) this project which is so useful for everyone.

Point #4 of Hmirov's speech should be discussed further. There were over 2000 locals at the pubic discussion on the subject of "waste-processing complex" (Eng) (which is mostly a landfill Russian "landfill") construction at Gladkoe settlement . Every single local voted against the project. Hmirov implied that the locals are stupid and selfish - both St. Petersburg and Leningrad region are in need of the new place for MSW. But when Hmirov have left the seminar he was mocked for the public discussion could pass far worse, because there were no schemas for the  "landfill" at the discussion.  Here you are examples of the "landfill" and the landfill (used in civilised countries).

Typical Russian "landfill"

Civilised landfill (source)

You do not need to know Russian to see the difference between a "landfill" and a landfill. In a "landfill" there are only natural barrier between waste and ground waters, no biogas collection, no adequate drainage system, etc. Frankly, there will be some kind of isolation between waste and  ground at Gladkoe and even wells for ground water monitoring and drinage of some kind, but definitely, there won't be any biogas collection (assessment of the environmental impact Rus).

I've read the environmental impact assessment - quite interesting... if you're looking for luls. Among staled and unreferenced Yandex maps, used for illustration there and tons of other bullshit this document dedicated to make statements about acceptability of estimated environmental impacts. So estimated landfill filtrate leak to the ground water (from landfill and precipitation pool) of 50 cubic meters per day(!!!), i.e. 18 250 per year  was found acceptable... ZOMFG!!! 0,00 cubic meters of this shit can be acceptable!!! This filtrate is the main landfill's threat to the environment and the point is keep it away from water bodies! So 18 250 m³ is not fucking acceptable! Ok, leaks are almost inevitable, but 50 m³ per day for the project where 44% of budget is dedicated to environment protection is far too much.

Back to the seminar. The next speaker was the deputy in the pink shirt (who will have to pass through elections soon).

Deputy in the pink shirt:

1. There is no junction to provide necessary transportation flow for new waste-plant at Janino settlement [place for another waste-treatment plant]. There are already terrible jams and if the wave of waste-trucks will be increased all the transportation between Vsevolozsk and St. Petersburg will just stuck.
2. There are no presses at the waste-collection stations, so more trucks used than it could be.
3. Nikol'sky, Ul'znovsky and Tosnenskiy districts of the Leningrad region already have the largest percentages of cancer amongst the population, so another source of the environmental impact (landfill at Gladkoe) is not desirable.
4.  It is better to build waste-treatment complex in place with the least population density, for example in Podporozskiy district of the Leningrad region. He used as an example an experience of Washington DC where all the collected waste is sent to California by train. [I wasn't able to find a proof-link for such practice]

Hmirov commented point#4 of the deputy speech: railway company has its own classification of waste [O_o] and once such option was considered: transportation costs were calculated at level of 2800 rubles (70 euro) per ton; but the destination was Appatity - town at Murmanskaya region.

I don't know why they wanted to move waste that far, but transportation inside the borders of Leningrad region will take at least 3 times less time so costs would not exceed 900 rubles (22,5 euro) per ton. It should be mentioned that expenditures for overall waste management in EU was about 26,73 euro per ton in 2006 (Anda et al., 2010 - Eurostat).

Speech of Viseberg wasn't interesting at all (he promoted technologies of the Recicling Association members), except he underlined issue of medical waste presence in MSW, but there were no proposals how to solve it; and separate waste collection - bins for separated waste are too small to produce profit for the transportation companies.

Conclusions. 

Seems that they are trying to solve the problems without any plan! So I know for sure the strategy  for waste management for St. Petersnurg and Leningrad region was developed twice in past 10 years. So they have to reread it or to redevelop it to have in mind every aspect of the waste management and develop them simultaneously to achieve to goals of environmental impacts minimisation.