ANCOVA interpretation – inconsistency between ANOVA results and pairwise group comparisons

I have some measurements (concentration) made in 4 groups (W, X, Y, Z) and time is my covariate. I make a linear model:

fit <- lm(concentration~group*year, data=data)

The results are as follows: ANOVA table:

anova(fit)

Analysis of Variance Table

Response: concentration
Df Sum Sq Mean Sq F value    Pr(>F)
group       3 3600.7 1200.22 32.6132 4.081e-10 *** #!
year        1  559.7  559.71 15.2087 0.0004311 ***
group:year  3   97.3   32.42  0.8809 0.4607155
Residuals  34 1251.3   36.80
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

and pairwise comparison:

summary(fit)
Call:
lm(formula = concentration ~ group * year, data = data)

Residuals:
Min     1Q Median     3Q    Max
-8.818 -4.019 -0.276  4.181 13.097

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  -433.0108   828.4293  -0.523    0.605
groupX      -1574.0090  1170.3741  -1.345    0.188 #!
groupY      -1666.3673  1170.3741  -1.424    0.164 #!
groupZ      -1201.2766  1170.3891  -1.026    0.312 #!
year            0.2418     0.4128   0.586    0.562
groupX:year     0.7937     0.5831   1.361    0.182
groupY:year     0.8409     0.5831   1.442    0.158
groupZ:year     0.6104     0.5831   1.047    0.303

Residual standard error: 6.066 on 34 degrees of freedom
Multiple R-squared:  0.7729,    Adjusted R-squared:  0.7261
F-statistic: 16.53 on 7 and 34 DF,  p-value: 2.852e-09

Now I have a problem in the interpretation of this data. As far as I understand, since the interaction in the ANOVA table is nonsignificant, I can check the group effect, and it is significant. This means that the intercept in different groups should be [significantly] different. But when I look to the summary table, there is no significant difference, at least – between group W and others (groupX, groupY and groupZ are nonsignificant). If I change the compared group from W to X or Y or Z the comparison results are still nonsignificant:

data2 <- data
data2$group[data2$group=="X"] <-"A"
fit <- lm(concentration~group*year, data=data2)
summary(fit)

Call:
lm(formula = concentration ~ group * year, data = data2)

Residuals:
Min     1Q Median     3Q    Max
-8.818 -4.019 -0.276  4.181 13.097

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.007e+03  8.267e+02  -2.428   0.0206 *
groupW       1.574e+03  1.170e+03   1.345   0.1876 #!
groupY      -9.236e+01  1.169e+03  -0.079   0.9375 #!
groupZ       3.727e+02  1.169e+03   0.319   0.7518 #!
year         1.035e+00  4.119e-01   2.514   0.0168 *
groupW:year -7.937e-01  5.831e-01  -1.361   0.1824
groupY:year  4.717e-02  5.825e-01   0.081   0.9359
groupZ:year -1.834e-01  5.825e-01  -0.315   0.7549
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.066 on 34 degrees of freedom
Multiple R-squared:  0.7729,    Adjusted R-squared:  0.7261
F-statistic: 16.53 on 7 and 34 DF,  p-value: 2.852e-09

data2 <- data
data2$group[data2$group=="Y"] <-"A"
fit <- lm(concentration~group*year, data=data2)
summary(fit)

Call:
lm(formula = concentration ~ group * year, data = data2)

Residuals:
Min     1Q Median     3Q    Max
-8.818 -4.019 -0.276  4.181 13.097

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.099e+03  8.267e+02  -2.539   0.0158 *
groupW       1.666e+03  1.170e+03   1.424   0.1636 #!
groupX       9.236e+01  1.169e+03   0.079   0.9375 #!
groupZ       4.651e+02  1.169e+03   0.398   0.6933 #!
year         1.083e+00  4.119e-01   2.628   0.0128 *
groupW:year -8.409e-01  5.831e-01  -1.442   0.1584
groupX:year -4.717e-02  5.825e-01  -0.081   0.9359
groupZ:year -2.305e-01  5.825e-01  -0.396   0.6948
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 6.066 on 34 degrees of freedom
Multiple R-squared:  0.7729,    Adjusted R-squared:  0.7261
F-statistic: 16.53 on 7 and 34 DF,  p-value: 2.852e-09

data2 <- data
data2$group[data2$group=="Z"] <-"A"
fit <- lm(concentration~group*year, data=data2)
summary(fit)

Call:
lm(formula = concentration ~ group * year, data = data2)

Residuals:
Min     1Q Median     3Q    Max
-8.818 -4.019 -0.276  4.181 13.097

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  -433.0108   828.4293  -0.523    0.605
groupX      -1574.0090  1170.3741  -1.345    0.188 #!
groupY      -1666.3673  1170.3741  -1.424    0.164 #!
groupZ      -1201.2766  1170.3891  -1.026    0.312 #!
year            0.2418     0.4128   0.586    0.562
groupX:year     0.7937     0.5831   1.361    0.182
groupY:year     0.8409     0.5831   1.442    0.158
groupZ:year     0.6104     0.5831   1.047    0.303

Residual standard error: 6.066 on 34 degrees of freedom
Multiple R-squared:  0.7729,    Adjusted R-squared:  0.7261
F-statistic: 16.53 on 7 and 34 DF,  p-value: 2.852e-09

How is it possible that there are no significant difference between any two groups when there is a significant group effect? Apparently my interpretation that significant group effect means that at least one group differ significantly from other in the intercept value is incorrect. So what is the correct interpretation of the significant group effect?